Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks.
Using AI to Recommend Pesticides for Effective Management of Multiple Plant D...IRJET Journal
This document discusses using artificial intelligence to recommend pesticides for effective plant disease management. It presents a methodology using computer vision and machine learning, specifically convolutional neural networks (CNNs), to develop a system for detecting plant diseases. The system would analyze leaf images using CNNs and provide fertilizer recommendations to help farmers more easily and quickly identify diseases affecting their crops. This could help reduce excessive pesticide use and environmental damage while improving crop yields. The paper reviews several related works applying CNNs and other machine learning methods to identify diseases from images. It discusses acquiring and preprocessing leaf image datasets to train models for large-scale disease detection, which could support more sustainable and data-driven agricultural decision making.
Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pretrained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
This document summarizes a research paper that proposes a system for detecting crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, sugarcane, wheat, grape, and rice. It uses a MobileNet CNN model trained on a dataset of leaf images. Experiments show the system can accurately classify leaf diseases with 97.33% precision. The system automatically diagnoses leaf diseases and recommends pesticides, helping farmers detect and address issues early.
Assessing the advancement of artificial intelligence and drones’ integration ...IJECEIAES
Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.
An Innovative Approach for Tomato Leaf Disease Identification and its Benefic...IRJET Journal
This document summarizes an innovative approach for identifying diseases in tomato leaves using image processing and machine learning techniques. Specifically, a Convolutional Neural Network (CNN) model is developed and trained on a dataset of tomato leaf images showing various disease symptoms. Through testing and validation, the proposed approach achieves high accuracy in classifying different types of tomato leaf diseases. Integrating this method could enable timely disease detection, reduce crop losses, and optimize resource allocation for more sustainable agricultural practices. The research contributes a practical solution for automating tomato leaf disease detection to enhance disease management and food security.
Early detection of tomato leaf diseases based on deep learning techniquesIAESIJAI
Tomato leaf diseases are a big issue for producers, and finding a single method to combat them is tough. Deep learning techniques, notably convolutional neural networks (CNNs), show promise in recognizing early indicators of illness, which can help producers avoid costly concerns in the future. In this study, we present a CNN-based model for the early identification of tomato leaf diseases to preserve output and boost yield. We used a dataset from the plantvillage database with 11,000 photos from 10 distinct disease categories to train our model. Our CNN was trained on this dataset, and the suggested model obtained an astounding 96% accuracy rate. This shows that our method has the potential to be efficient in detecting tomato leaf diseases early on, therefore assisting producers in managing and reducing disease outbreaks and, as a result, resulting in higher crop yields.
The use of pesticides in agriculture is essential to maintain the quality of large scale production. The spraying of these products by using aircraft speeds up the process and prevents compacting of the soil. However, adverse weather conditions e.g. the speed and direction of the wind can impair the effectiveness of the spraying of pesticides in a target crop field. Thus, there is a risk that the pesticide can drift to neighboring crop fields. It is believed that a large amount of all the pesticide used in the world drifts outside of the target crop field and only a small amount is effective in controlling pests. However, with increased precision in the spraying, it is possible to reduce the amount of pesticide used and improve the quality of agricultural products as well as mitigate the risk of environmental damage. In the past several years, UAV has been extensively used in agriculture. However, the efficiency is still not as high as desired and the phenomenon of pesticide pollution is still existing. This is mainly because of the following two problems 1 the autonomy of most existing UAV system is still very limited. Actually, most of them are still operated through remote controlling. 2 the UAVs operating precision is not high enough due to the low accuracy flight control near the plants. The paper presents combination of new approaches and technologies in modern day agriculture. Perspectives and benefits of usage of Unmanned Aerial Vehicles in different spheres of agriculture considered on the base of spraying drone project called “AeroDroneâ€. Kislaya Anand | Goutam R. ""An Autonomous UAV for Pesticide Spraying"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23161.pdf
Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/23161/an-autonomous-uav-for-pesticide-spraying/kislaya-anand
Analysis and prediction of seed quality using machine learning IJECEIAES
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Using AI to Recommend Pesticides for Effective Management of Multiple Plant D...IRJET Journal
This document discusses using artificial intelligence to recommend pesticides for effective plant disease management. It presents a methodology using computer vision and machine learning, specifically convolutional neural networks (CNNs), to develop a system for detecting plant diseases. The system would analyze leaf images using CNNs and provide fertilizer recommendations to help farmers more easily and quickly identify diseases affecting their crops. This could help reduce excessive pesticide use and environmental damage while improving crop yields. The paper reviews several related works applying CNNs and other machine learning methods to identify diseases from images. It discusses acquiring and preprocessing leaf image datasets to train models for large-scale disease detection, which could support more sustainable and data-driven agricultural decision making.
Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pretrained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
This document summarizes a research paper that proposes a system for detecting crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, sugarcane, wheat, grape, and rice. It uses a MobileNet CNN model trained on a dataset of leaf images. Experiments show the system can accurately classify leaf diseases with 97.33% precision. The system automatically diagnoses leaf diseases and recommends pesticides, helping farmers detect and address issues early.
Assessing the advancement of artificial intelligence and drones’ integration ...IJECEIAES
Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.
An Innovative Approach for Tomato Leaf Disease Identification and its Benefic...IRJET Journal
This document summarizes an innovative approach for identifying diseases in tomato leaves using image processing and machine learning techniques. Specifically, a Convolutional Neural Network (CNN) model is developed and trained on a dataset of tomato leaf images showing various disease symptoms. Through testing and validation, the proposed approach achieves high accuracy in classifying different types of tomato leaf diseases. Integrating this method could enable timely disease detection, reduce crop losses, and optimize resource allocation for more sustainable agricultural practices. The research contributes a practical solution for automating tomato leaf disease detection to enhance disease management and food security.
Early detection of tomato leaf diseases based on deep learning techniquesIAESIJAI
Tomato leaf diseases are a big issue for producers, and finding a single method to combat them is tough. Deep learning techniques, notably convolutional neural networks (CNNs), show promise in recognizing early indicators of illness, which can help producers avoid costly concerns in the future. In this study, we present a CNN-based model for the early identification of tomato leaf diseases to preserve output and boost yield. We used a dataset from the plantvillage database with 11,000 photos from 10 distinct disease categories to train our model. Our CNN was trained on this dataset, and the suggested model obtained an astounding 96% accuracy rate. This shows that our method has the potential to be efficient in detecting tomato leaf diseases early on, therefore assisting producers in managing and reducing disease outbreaks and, as a result, resulting in higher crop yields.
The use of pesticides in agriculture is essential to maintain the quality of large scale production. The spraying of these products by using aircraft speeds up the process and prevents compacting of the soil. However, adverse weather conditions e.g. the speed and direction of the wind can impair the effectiveness of the spraying of pesticides in a target crop field. Thus, there is a risk that the pesticide can drift to neighboring crop fields. It is believed that a large amount of all the pesticide used in the world drifts outside of the target crop field and only a small amount is effective in controlling pests. However, with increased precision in the spraying, it is possible to reduce the amount of pesticide used and improve the quality of agricultural products as well as mitigate the risk of environmental damage. In the past several years, UAV has been extensively used in agriculture. However, the efficiency is still not as high as desired and the phenomenon of pesticide pollution is still existing. This is mainly because of the following two problems 1 the autonomy of most existing UAV system is still very limited. Actually, most of them are still operated through remote controlling. 2 the UAVs operating precision is not high enough due to the low accuracy flight control near the plants. The paper presents combination of new approaches and technologies in modern day agriculture. Perspectives and benefits of usage of Unmanned Aerial Vehicles in different spheres of agriculture considered on the base of spraying drone project called “AeroDroneâ€. Kislaya Anand | Goutam R. ""An Autonomous UAV for Pesticide Spraying"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23161.pdf
Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/23161/an-autonomous-uav-for-pesticide-spraying/kislaya-anand
Analysis and prediction of seed quality using machine learning IJECEIAES
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Peanut leaf spot disease identification using pre-trained deep convolutional...IJECEIAES
Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
التقدم في تكنولوجيا المعلومات للكشف عن أمراض النباتات Advances in Informatio...Mohamed Mostafa
This document discusses advances in information technology for detecting plant diseases. It outlines how machine learning techniques and expert systems can be used to identify plant diseases early. Remote sensing using hyperspectral sensors and wireless sensor networks are also presented as methods to continuously monitor crops for stress or disease. The conclusion states that sensory-based information and communication technologies can help experts more accurately detect problems affecting crops.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
Detection of Plant Diseases Using CNN ArchitecturesIRJET Journal
The document discusses using convolutional neural network (CNN) architectures to detect plant diseases through images of plant leaves. It evaluates several CNN models - ResNet-50, Efficient-B2, and VGG-16 - on a dataset of 87,000 plant images from Kaggle with 38 disease categories. Efficient-B2 achieved the highest accuracy of 94% for classifying 250 healthy and 250 diseased leaf images. The document recommends CNNs for efficiently detecting plant diseases at early stages from leaf images in order to increase crop yields.
Plant Leaf Diseases Identification in Deep LearningCSEIJJournal
Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in
many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant
leaf detection made possible by deep learning has paved the way for smartphone-assisted disease
diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under
controlled conditions, we train a deep convolutional neural network to spot one crop species and
4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% on a held-out test set,
demonstrating the feasibility of this approach. Overall, the approach of coaching deep learning models on
increasingly large and publicly available image datasets presents a transparent path toward smartphone-
assisted crop disease diagnosis on a large global scale. After the disease is successfully predicted with a
decent confidence level, the corresponding remedy for the disease present is displayed that may be taken as
a cure.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
SURVEY PAPER ON CROP DISEASE NOTIFICATION SYSTEMIRJET Journal
This document summarizes a survey paper on a crop disease notification system that uses deep learning and computer vision on smartphones. The system is trained on a dataset of over 20,000 photos of plant leaves with 38 different disease categories across 15 crop species. A convolutional neural network achieves 85.35% accuracy at identifying diseases. The system is designed for distribution between smartphones and cloud servers, allowing farmers to take photos of diseased plants and receive notifications of the detected disease. The goal is to help farmers identify diseases early and apply the proper treatments to maximize crop yields.
Improved vision-based diagnosis of multi-plant disease using an ensemble of d...IJECEIAES
Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
The document proposes developing a system called the Layer Bird Vaccination Monitoring & Disease Detection System. This system would help small-scale layer poultry farmers in Zimbabwe track vaccinations, monitor treatments, and detect diseases early using data visualization and machine learning models. The system aims to address challenges small-scale farmers face like a lack of record keeping, monitoring of bird health, and limited access to veterinary support. It would allow farmers to enter bird symptom data and get recommendations to prevent losses from diseases.
A deep learning-based mobile app system for visual identification of tomato p...IJECEIAES
Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.
Final Year Project CHP 1& 2 CHENAI MAKOKO.docxChenaiMartha
The document proposes developing a model for early detection of layer bird diseases for layer poultry farmers. It discusses challenges small-scale farmers face in detecting diseases early due to limited access to veterinary support. Existing systems for disease detection include expert systems using certainty factors, deep learning models for detecting diseases from fecal images, and IoT-based frameworks. However, these systems either focus on expert diagnosis, rely on large datasets, or require specialized hardware. The proposed model aims to allow farmers to enter symptoms and receive recommendations to aid early disease detection.
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
This document describes a proposed pesticide recommendation system for cotton crops that predicts diseases due to climatic changes using a decision tree algorithm. The system would help farmers determine which pesticides to use for crop diseases. It collects data on weather conditions, pests, symptoms and recommended pesticides. This data is preprocessed, features are selected, and a decision tree is generated to classify the data and induce rules. The rules and a graphical user interface would allow farmers to query the system and receive pesticide recommendations based on weather and pest information. The goal is to improve crop productivity and farmer profits by predicting diseases and providing targeted pesticide advice.
A comparative study of mango fruit pest and disease recognitionTELKOMNIKA JOURNAL
Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
Leaf Disease Detection Using Image Processing and MLIRJET Journal
The document discusses using image processing and machine learning techniques like convolutional neural networks (CNNs) to detect plant leaf diseases. It proposes a system that uses CNNs to classify plant leaf images and detect diseases. The system would first preprocess leaf images, then extract features from them and feed them into a CNN model for classification. This could help farmers detect diseases early and improve crop productivity. The document reviews several related works applying CNNs and deep learning to tasks like mango leaf disease detection, tomato disease detection, and dragon fruit maturity detection with high accuracy. It outlines the proposed system architecture and algorithm and concludes CNNs can accurately detect plant diseases with reduced time and cost compared to manual methods.
The Plant Phenome Journal - 2019 - Wu - Autonomous Detection of Plant Disease...VinayMishra830532
This document summarizes a study that used a deep learning model to detect plant disease symptoms in aerial images taken by drones with high accuracy. The researchers trained a convolutional neural network on lower resolution images to identify northern leaf blight lesions in maize plants. They were able to detect lesions at a fine spatial scale and produce heat maps indicating lesion locations over entire aerial images with 95.1% accuracy. This method provides a potential new way to rapidly detect and quantify an economically important plant disease across large areas for the purposes of plant breeding and crop management.
A study on real time plant disease diagonsis systemIJARIIT
The document discusses developing a real-time plant disease diagnosis mobile application. It aims to allow farmers to easily capture images of plant leaves using a mobile camera, send the images to a central system for analysis, and receive diagnoses and treatment recommendations. The proposed system would use image processing and data mining techniques to analyze leaf images for abnormalities, identify the plant species, recognize any diseases present, and recommend appropriate pesticides and estimate treatment costs. This would provide a low-cost, convenient solution for farmers to quickly diagnose and respond to plant diseases.
Abstract— Today, fruit science have well been established in world trade networks and sophisticated cultural and postharvest technologies that allow fruits to be enjoyed throughout much of the year, instead of mere weeks per year like our ancestors experienced. Especially modern biotechnological methods including genetic engineering technologies have been taken part in breeding strategies of fruit crops. Several biotechnological methods can be applied to plant to have better ones in the process of fruit breeding. Genetic engineering is a powerful tool for plant improvement and has the potential to allow the integration of desirable characteristics into existing genomes. Transformation technology developed a path to transfer important genes into plant genome for enhancing resistance against fungal, viral pathogens, other pests, drought, and salinity as well as silencing undesirable genes and improvement in nutrient acquisition. Different gene transfer techniques could be employed for fruit species. As well as direct and indirect transformation, modern genome editing methods recently have been used in plant science. In this review, we illustrated how to use these technologies in fruit science.
Detection of diseases in rice leaf using convolutional neural network with tr...IJECEIAES
Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Mais conteúdo relacionado
Semelhante a Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery
Peanut leaf spot disease identification using pre-trained deep convolutional...IJECEIAES
Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
التقدم في تكنولوجيا المعلومات للكشف عن أمراض النباتات Advances in Informatio...Mohamed Mostafa
This document discusses advances in information technology for detecting plant diseases. It outlines how machine learning techniques and expert systems can be used to identify plant diseases early. Remote sensing using hyperspectral sensors and wireless sensor networks are also presented as methods to continuously monitor crops for stress or disease. The conclusion states that sensory-based information and communication technologies can help experts more accurately detect problems affecting crops.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
Detection of Plant Diseases Using CNN ArchitecturesIRJET Journal
The document discusses using convolutional neural network (CNN) architectures to detect plant diseases through images of plant leaves. It evaluates several CNN models - ResNet-50, Efficient-B2, and VGG-16 - on a dataset of 87,000 plant images from Kaggle with 38 disease categories. Efficient-B2 achieved the highest accuracy of 94% for classifying 250 healthy and 250 diseased leaf images. The document recommends CNNs for efficiently detecting plant diseases at early stages from leaf images in order to increase crop yields.
Plant Leaf Diseases Identification in Deep LearningCSEIJJournal
Crop diseases constitute a big threat to plant existence, but their rapid identification remains difficult in
many parts of the planet because of the shortage of the required infrastructure. In computer vision, plant
leaf detection made possible by deep learning has paved the way for smartphone-assisted disease
diagnosis. employing a public dataset of 4,306 images of diseased and healthy plant leaves collected under
controlled conditions, we train a deep convolutional neural network to spot one crop species and
4 diseases (or absence thereof). The trained model achieves an accuracy of 97.35% on a held-out test set,
demonstrating the feasibility of this approach. Overall, the approach of coaching deep learning models on
increasingly large and publicly available image datasets presents a transparent path toward smartphone-
assisted crop disease diagnosis on a large global scale. After the disease is successfully predicted with a
decent confidence level, the corresponding remedy for the disease present is displayed that may be taken as
a cure.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
SURVEY PAPER ON CROP DISEASE NOTIFICATION SYSTEMIRJET Journal
This document summarizes a survey paper on a crop disease notification system that uses deep learning and computer vision on smartphones. The system is trained on a dataset of over 20,000 photos of plant leaves with 38 different disease categories across 15 crop species. A convolutional neural network achieves 85.35% accuracy at identifying diseases. The system is designed for distribution between smartphones and cloud servers, allowing farmers to take photos of diseased plants and receive notifications of the detected disease. The goal is to help farmers identify diseases early and apply the proper treatments to maximize crop yields.
Improved vision-based diagnosis of multi-plant disease using an ensemble of d...IJECEIAES
Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
The document proposes developing a system called the Layer Bird Vaccination Monitoring & Disease Detection System. This system would help small-scale layer poultry farmers in Zimbabwe track vaccinations, monitor treatments, and detect diseases early using data visualization and machine learning models. The system aims to address challenges small-scale farmers face like a lack of record keeping, monitoring of bird health, and limited access to veterinary support. It would allow farmers to enter bird symptom data and get recommendations to prevent losses from diseases.
A deep learning-based mobile app system for visual identification of tomato p...IJECEIAES
Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.
Final Year Project CHP 1& 2 CHENAI MAKOKO.docxChenaiMartha
The document proposes developing a model for early detection of layer bird diseases for layer poultry farmers. It discusses challenges small-scale farmers face in detecting diseases early due to limited access to veterinary support. Existing systems for disease detection include expert systems using certainty factors, deep learning models for detecting diseases from fecal images, and IoT-based frameworks. However, these systems either focus on expert diagnosis, rely on large datasets, or require specialized hardware. The proposed model aims to allow farmers to enter symptoms and receive recommendations to aid early disease detection.
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
This document describes a proposed pesticide recommendation system for cotton crops that predicts diseases due to climatic changes using a decision tree algorithm. The system would help farmers determine which pesticides to use for crop diseases. It collects data on weather conditions, pests, symptoms and recommended pesticides. This data is preprocessed, features are selected, and a decision tree is generated to classify the data and induce rules. The rules and a graphical user interface would allow farmers to query the system and receive pesticide recommendations based on weather and pest information. The goal is to improve crop productivity and farmer profits by predicting diseases and providing targeted pesticide advice.
A comparative study of mango fruit pest and disease recognitionTELKOMNIKA JOURNAL
Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
Leaf Disease Detection Using Image Processing and MLIRJET Journal
The document discusses using image processing and machine learning techniques like convolutional neural networks (CNNs) to detect plant leaf diseases. It proposes a system that uses CNNs to classify plant leaf images and detect diseases. The system would first preprocess leaf images, then extract features from them and feed them into a CNN model for classification. This could help farmers detect diseases early and improve crop productivity. The document reviews several related works applying CNNs and deep learning to tasks like mango leaf disease detection, tomato disease detection, and dragon fruit maturity detection with high accuracy. It outlines the proposed system architecture and algorithm and concludes CNNs can accurately detect plant diseases with reduced time and cost compared to manual methods.
The Plant Phenome Journal - 2019 - Wu - Autonomous Detection of Plant Disease...VinayMishra830532
This document summarizes a study that used a deep learning model to detect plant disease symptoms in aerial images taken by drones with high accuracy. The researchers trained a convolutional neural network on lower resolution images to identify northern leaf blight lesions in maize plants. They were able to detect lesions at a fine spatial scale and produce heat maps indicating lesion locations over entire aerial images with 95.1% accuracy. This method provides a potential new way to rapidly detect and quantify an economically important plant disease across large areas for the purposes of plant breeding and crop management.
A study on real time plant disease diagonsis systemIJARIIT
The document discusses developing a real-time plant disease diagnosis mobile application. It aims to allow farmers to easily capture images of plant leaves using a mobile camera, send the images to a central system for analysis, and receive diagnoses and treatment recommendations. The proposed system would use image processing and data mining techniques to analyze leaf images for abnormalities, identify the plant species, recognize any diseases present, and recommend appropriate pesticides and estimate treatment costs. This would provide a low-cost, convenient solution for farmers to quickly diagnose and respond to plant diseases.
Abstract— Today, fruit science have well been established in world trade networks and sophisticated cultural and postharvest technologies that allow fruits to be enjoyed throughout much of the year, instead of mere weeks per year like our ancestors experienced. Especially modern biotechnological methods including genetic engineering technologies have been taken part in breeding strategies of fruit crops. Several biotechnological methods can be applied to plant to have better ones in the process of fruit breeding. Genetic engineering is a powerful tool for plant improvement and has the potential to allow the integration of desirable characteristics into existing genomes. Transformation technology developed a path to transfer important genes into plant genome for enhancing resistance against fungal, viral pathogens, other pests, drought, and salinity as well as silencing undesirable genes and improvement in nutrient acquisition. Different gene transfer techniques could be employed for fruit species. As well as direct and indirect transformation, modern genome editing methods recently have been used in plant science. In this review, we illustrated how to use these technologies in fruit science.
Detection of diseases in rice leaf using convolutional neural network with tr...IJECEIAES
Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.
Semelhante a Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 891~903
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp891-903 891
Journal homepage: http://ijece.iaescore.com
Optimizing olive disease classification through transfer learning
with unmanned aerial vehicle imagery
El Mehdi Raouhi1
, Mohamed Lachgar1
, Hamid Hrimech2
, Ali Kartit1
1
LTI Laboratory, National School of Applied Sciences (ENSA), University Chouaib Doukkali, El Jadida, Morocco
2
LAMSAD Laboratory, National School of Applied Sciences (ENSA), University Hassan First, Berrechid, Morocco
Article Info ABSTRACT
Article history:
Received May 2, 2023
Revised Aug 8, 2023
Accepted Sep 3, 2023
Early detection of diseases in growing olive trees is essential for reducing
costs and increasing productivity in this crucial economic activity. The
quality and quantity of olive oil depend on the health of the fruit, making
accurate and timely information on olive tree diseases critical to monitor
growth and anticipate fruit output. The use of unmanned aerial vehicles
(UAVs) and deep learning (DL) has made it possible to quickly monitor
olive diseases over a large area indeed of limited sampling methods.
Moreover, the limited number of research studies on olive disease detection
has motivated us to enrich the literature with this work by introducing new
disease classes and classification methods for this tree. In this study, we
present a UAV system using convolutional neuronal network (CNN) and
transfer learning (TL). We constructed an olive disease dataset of 14K
images, processed and trained it with various CNN in addition to the
proposed MobileNet-TL for improved classification and generalization. The
simulation results confirm that this model allows for efficient diseases
classification, with a precision accuracy achieving 99% in validation. In
summary, TL has a positive impact on MobileNet architecture by improving
its performance and reducing the training time for new tasks.
Keywords:
Classification
Convolutional neural network
Olive diseases
Sensors
Transfer learning
Unmanned aerial vehicles
This is an open access article under the CC BY-SA license.
Corresponding Author:
El Mehdi Raouhi
LTI Laboratory, National School of Applied Sciences (ENSA), University Chouaib Doukkali
El Jadida, Morocco
Email: raouhi.e@ucd.ac.ma
1. INTRODUCTION
The evolution of agriculture technology has not only played a pivotal role in transforming food
production but has also significantly enhanced farming practices, ushering in more efficient methods for
planting, harvesting, and managing crops and livestock [1]. These technological advances have led to
increased yields and improved crop quality, effectively meeting the growing global demand for food while
also minimizing the environmental impact of agriculture [2]. Key examples of agricultural technology
include precision farming, drones, genetic engineering, and smart irrigation systems, empowering farmers to
make informed decisions, optimize yields, minimize waste, and conserve natural resources [3]. In the context
of unmanned aerial vehicle (UAV)-enabled olive disease classification, transfer learning has the potential to
revolutionize disease detection. By using pre-trained models on large-scale datasets from related domains,
such as general plant pathology or agricultural images, we can exploit the learned features and
representations to adapt to the specific olive disease classification task [4]. While several studies have
explored UAV applications in agriculture [5]–[9], including disease detection, the use of transfer learning
specifically for olive disease classification represents a novel approach that can significantly enhance the
accuracy and speed of disease identification. This research aims to bridge the gap between traditional disease
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 891-903
892
assessment methods and cutting-edge technology, ultimately benefiting olive growers and the agricultural
community at large. Amidst these remarkable technological advancements, the field of olive disease
classification has emerged as a critical component in identifying and categorizing diseases affecting olive
trees. By standardizing disease descriptions and diagnoses, classification systems facilitate effective
communication among researchers, extension workers, and farmers [10]. Among these systems, the
international code of nomenclature for cultivated plants (ICNCP) classifies olive diseases based on affected
plant parts and symptoms, while other systems categorize diseases based on their causative agents, including
fungal, bacterial, viral, and phytoplasma diseases. Examples of olive diseases include Verticillium wilt,
Xylella Fastidiosa, and olive knot. Precise disease management hinges upon accurate diagnosis facilitated by
these classification systems, enabling the implementation of targeted strategies such as selecting disease-
resistant cultivars, practicing crop rotation, and employing appropriate fungicides and control measures [11].
The continuous expansion of olive cultivation across the Mediterranean region, spanning approximately 750
million hectares, faces significant challenges from various factors, including insects, nematodes, and
pathogens. In particular, pathogenic agents and pests pose a substantial threat to olive crop yields in the
European Union, exacerbated by factors such as commercialization, climate change, and evolving
agricultural practices. Consequently, there is an urgent need for advanced solutions in disease detection and
classification to mitigate these adverse effects and safeguard the productivity and sustainability of olive
production [12]. Recent breakthroughs in computer vision and machine learning have revolutionized disease
classification across diverse domains. One particularly promising technique is transfer learning (TL), which
harnesses pre-trained deep neural networks (DNNs) to address novel classification tasks with limited data
[13]. In the context of UAV-enabled olive disease classification, transfer learning presents a transformative
opportunity. By leveraging pre-trained models from vast datasets in related domains, such as general plant
pathology or agricultural images, transfer learning enables the adaptation of learned features and
representations to accurately classify olive diseases. This article aims to capitalize on the synergistic potential
between cutting-edge drone technology and transfer learning techniques for smart agriculture development,
with a specific focus on olive disease classification. Our research seeks to expand existing knowledge and
provide novel insights that will prove invaluable to olive growers and the broader agricultural community
[14], [15]. The upcoming sections of this article are organized in the following manner: section 2 presents an
in-depth exploration of the theoretical background of deep learning (DL), while section 3 reviews recent
related works. Section 4 presents an overview of the method and simulation workflow used in our research.
Finally, section 5 delves into the experimentation results for olive disease classification, followed by a
comprehensive discussion of the contributions and challenges faced. By showcasing the relevance and
effectiveness of UAV-enabled olive disease classification based on transfer learning, our research aims to
drive more efficient and data-driven smart agriculture practices in the olive industry.
2. BACKGROUND
2.1. Convolutional neuronal network
CNN is an abbreviation for convolutional neural network, a deep learning algorithm widely
employed for image classification and object recognition. CNNs are designed to handle image-based data and
are particularly effective for image classification problems. They draw inspiration from the structure and
function of the visual cortex in the human brain and are comprised of multiple layers of artificial neurons that
learn to identify patterns in images. CNNs have found applications in diverse fields, such as computer vision,
natural language processing, and speech recognition [16].
2.2. Transfer learning
Transfer learning (or learning by transfer) allows deep learning to be performed without the need for
a month of computations. The idea is to leverage the information gained by a neural network while solving
one issue to solve another that is similar but not identical. As a result, knowledge is transferred. Also, transfer
learning prevents overfitting while also speeding up network training. When the number of input photos is
minimal, a strong recommendation is to avoid training the neural network from scratch (i.e., with random
initialization) due to the considerably larger number of parameters to learn compared to the number of
images. This approach carries a high risk of overfitting [17].
2.3. Unmanned aerial systems for agriculture
Unmanned aerial systems (UAS), commonly referred to as drones, have revolutionized the way
agriculture is conducted. These systems have made it possible to monitor crops and livestock with great
precision and efficiency. UAS can capture aerial images and data, providing farmers with valuable
information on crop health, soil moisture, and nutrient levels. Utilizing this information enables informed
3. Int J Elec & Comp Eng ISSN: 2088-8708
Optimizing olive disease classification through transfer learning with unmanned aerial … (El Mehdi Raouhi)
893
decisions on crop management, such as fertilization and irrigation, leading to increased productivity and
reduced costs. Moreover, UAS can rapidly cover extensive areas, making it possible to identify potential
issues early on and take corrective action before they escalate [18].
3. RELATED WORKS
3.1. Techniques for plant diseases classification based on deep learning
Research works in the field of olive disease classification is very limited in number, hence the
integration of plant disease classification review in general and olive in particular. First, Bi et al. [19] design a
system that can identify apples are classified according to their color and distinct specular reflection patterns.
Additional information like average apple size is used to weed out incorrect results or to account for many
apples growing areas. Second, Prasetyo et al. [20] employed the ResNet-9 architecture to construct an optimal
CNN model for classifying corn plant diseases. They conducted comparisons across various epochs to
determine the best model, with the highest accuracy achieved at the 100th
epoch. Moreover, Singh et al. [21]
present an extensive study on the plant village dataset, whose images were collected using a formalized process,
with highly perfect post-processing background development results, when the images were acquired under real
conditions. It is also necessary to mention the work of Jadon [22] that explain the difficulty of learning and
performance on relatively small volumes of data. This is an important parameter affecting the quality of the
results achieved through the contribution of this study. In the same way, Tassis et al. [23] considered a dataset
consisting of several plant types with different sample size characteristics. This is used to challenge the
performance of CNNs under various conditions. Deep learning networks composed of different dataset sizes
allow for enhanced comprehension of the advantages and limitations of these types of networks. Also
noteworthy is the relevant work of Long et al. [24] that review methods developed for inductive transfer
learning using convolutional networks. In addition, inductive transfer learning was then studied by Li et al. [25],
where they describe the notion of regularization and tuning parameters to improve the performance of the target
model. The mentioned research articles in Table 1 focus on developing deep learning models for the detection
and classification of olive tree diseases using deep learning techniques. Alshammari et al. [3] proposed a
method based on both the vision transformer (ViT) and CNN models for olive disease classification. They
achieved high accuracy in detecting multiple types of diseases. In another study by Alshammari et al. [12], an
optimized deep learning approach winged optimized artificial neural network (WOA-ANN) was developed for
the identification of olive leaf diseases. The proposed method used a transfer learning technique and achieved
higher accuracy than previous studies. Also, Ksibi et al. [26] proposed a hybrid deep learning model called
mobile residual neural network (MobiRes-Net) for detecting and classifying olive leaf diseases. Their model
combined the advantages of MobileNet and ResNet architectures and achieved high accuracy in detecting
multiple types of diseases. In addition, Uğuz and Uysal [10] developed a deep convolutional neural network
based on visual geometry group (VGG) for classifying olive leaf diseases. Their model achieved high accuracy
in identifying four types of diseases. Uğuz [27] proposed an automatic olive peacock spot disease recognition
system using a single shot detector (SSD) method. The proposed method achieved high accuracy in detecting
this disease. On the same direction [28] developed an efficient model for olive disease detection. They used
transfer learning and achieved high accuracy in detecting three types of diseases. Finally, Milicevic et al. [29]
developed deep learning models designed to identify the flowering phenophase in olive trees. They achieved
high accuracy in detecting the flowering phenophase, which can be used for predicting fruit yield. Collectively,
these studies showcase the potential of deep learning models in accurately detecting and classifying olive tree
diseases, which can help in the early detection and management of these diseases to improve crop yield and
reduce economic losses.
Table 1. Comparison of performance on olive diseases classification systems
Ref Paper Validation accuracy Augmentation Transfer learning CNN architecture
[3] Alshammari et al. 2022 96% Yes Yes Vision transformer
[10] Uğuz and Uysal 2020 95% Yes Yes VGG
[12] Alshammari et al. 2023 99% Yes Yes WOA-ANN
[26] Ksibi et al. 2022 97.08% Yes Yes MobiRes-Net
[27] Uğuz 2020 96% Yes No SSD
[28] Alruwaili et al. 2019 99.11% Yes No Alexnet
[29] Milicevic et al. 2020 97.20% Yes No VGG-inspired network
3.2. Techniques for plant diseases classification based on UAV imagery
This section focuses on classification studies that utilize unmanned aerial vehicle (UAV) imagery to
detect olive tree diseases, which have been relatively scarce in the literature. The studies cited in Table 2
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 891-903
894
explore the applications of UAVs and multispectral imagery in olive tree cultivation. The papers present
various findings, including the use of different algorithms for classification, the incorporation of ground truth
data, and the use of different spectral bands for disease detection. Overall, the studies demonstrate the
potential of UAV-based classification techniques for detecting olive tree diseases and improving agricultural
management practices. First, Šiljeg et al. [5] used geographic object-based image analysis with randomized
truncated cluster (GEOBIA RTC) and vegetation indices to extract olive tree canopies from very
high-resolution UAV multispectral imagery. They achieved high accuracy in detecting olive trees, and their
approach can potentially be used for precision agriculture and monitoring of olive trees. Then in [6]
developed a residual neural network (ResNet50) for classifying olive tree cases based on UAV imagery.
Their model achieved high accuracy and can potentially be used for monitoring the growth and health of
olive trees. After that, Nisio et al. [7] follow an approach using latent Dirichlet allocation (LDA) on all the
available spectral information. The performance in classification was excellent, achieving a sensitivity of
98% and precision of 100% on a test set that comprises 71 trees, 75% of which were afflicted. Also, Jurado
[30] used multispectral mapping to characterize individual olive trees. Their approach can potentially be used
for precision agriculture, monitoring tree growth and health, and optimizing orchard management. In
addition, Rallo et al. [31] explored the use of UAV imagery to support genotype selection in olive breeding
programs. They found that UAV imagery can potentially provide valuable information for selecting superior
olive genotypes based on traits such as canopy volume and shape. Castrignanò et al. [8] used UAV multi-
resolution image segmentation with mask regions with convolutional neural networks (Mask R-CNN) to
estimate olive tree biovolume. Safonova et al. [9] devised a rapid detection technique to identify Xylella
Fastidiosa-infected olive trees using multispectral imaging from UAVs. Their method holds promise for early
detection and continuous monitoring of this detrimental plant pathogen in olive trees. Additionally, Neupane
and Baysal-Gurel [32] introduced a semi-automatic approach for the early detection of Xylella Fastidiosa in
olive trees, leveraging UAV multispectral imagery and geostatistical-discriminant analysis. Their approach
achieved high accuracy in detecting Xylella Fastidiosa-infected trees.
Table 2. Comparison of performance on drone-based similar classification systems
Ref Paper Validation accuracy Augmentation Transfer learning CNN architecture
[5] Šiljeg et al. 2023 88% Yes No GEOBIA RTC
[6] Sehree and Khidhir 2022 97.2% Yes No ResNet50
[7] Nisio et al. 2020 98% Yes No LDA
[8] Castrignanò et al. 2020 77% No No LDA
[9] Safonova et al. 2021 95% Yes No Mask R-CNN
4. METHOD
4.1. Drone description
In Figure 1, we are presented with a striking image of a drone, thoughtfully equipped with an
impressive array of sensors. Among these sensors, the forward-facing and downward-facing ones hold
particular significance, serving as crucial components for the Mavic Pro sophisticated obstacle detection and
avoidance capabilities. Proudly manufactured by DJI, the Mavic 2 Pro takes center stage as a top-of-the-line
UAV. With its state-of-the-art technology and cutting-edge features, the Mavic 2 Pro unquestionably ranks
among the most advanced and coveted drones available in the market today [32].
4.2. Simulation workflow
The simulation architecture employed in our study encompasses a well-structured series of tasks
crucial for image classification techniques, as illustrated in the schematic diagram presented in Figure 2.
Initially, meticulous attention is given to adjusting the flight plan of the UAV to ensure precise and
comprehensive image capture of the olive trees within the study area, yielding high-quality data for analysis.
Subsequently, the data collection phase commences, wherein the UAV carries out image acquisition,
capturing multispectral images of the olive trees from different perspectives. To prepare the collected data for
classification, thorough preprocessing steps are undertaken to eliminate any unwanted noise or artifacts that
might impede the accuracy of the classification process. The subsequent steps revolve around selecting
suitable classifiers and feature extraction methods that can effectively and accurately identify and classify the
various olive tree diseases present in the collected images. Diverse training modes are thoughtfully chosen to
optimize the selected classifiers’ performance, empowering the model to deliver precise disease classification
results. Moreover, an optimization algorithm is applied to further enhance the classification model’s
accuracy. To refine and fine-tune the classification results, comprehensive post-classification processing is
conducted, aiming to ensure the highest level of accuracy and reliability in disease identification. The
5. Int J Elec & Comp Eng ISSN: 2088-8708
Optimizing olive disease classification through transfer learning with unmanned aerial … (El Mehdi Raouhi)
895
performance of the model is meticulously evaluated using a range of essential metrics providing
comprehensive insights into the model’s efficacy. Overall, this simulation architecture is meticulously
designed to ensure the accurate and efficient classification of olive tree diseases, leveraging the cutting-edge
combination of UAV-based image acquisition and deep learning techniques. The seamless integration of
these components empowers our study to unlock novel and invaluable insights into disease detection and
management, paving the way for sustainable and optimized olive crop production.
Figure 1. MAVIC 2 PRO drone and sensor deployed for this research
Figure 2. General schema for experimental studies
4.3. Study area
The study area highlighted in Figure 3 is the olive groves in the Béni Mellal-Khénifra region, which
is situated in the central part of the country and covers an area of 17,125 square kilometers. This region is
characterized by a very continental climate with an average altitude of 400 to 700 meters and precipitation
that ranges from 300 to 750 mm, varying on the year. The main activity in the region is agriculture, which
accounts for 81% of the active rural population in 2008 and has a profound effect on the regional economy,
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 891-903
896
especially in the plains (Tadla) that have abundant water resources suitable for modern and industrial
agriculture development. The region’s suitability for olive cultivation is based on favorable climatic and
geological conditions, as well as expertise in olive oil production, while the number of olive varieties present
is an additional factor, but not the sole justification [33].
Figure 3. Study area in the Béni Mellal-Khénifra region
4.4. Drone flight
Planning drone flight planning is an essential process that involves preparing for and executing a
safe and efficient drone flight. This process includes several steps, such as researching the flight area,
checking local regulations and restrictions, obtaining necessary permits, ensuring the drone is in good
working condition, choosing a safe and efficient flight path, checking the weather conditions, inspecting the
drone before flight, establishing communication with individuals in the area, flying the drone following the
planned route, monitoring the drone’s flight status, and evaluating the flight after completion. Once the flight
plan is set as presented in Figure 4, the Mavic Pro 2 will take off and follow the set plan. During the flight,
adjustments can be made if necessary, using the DJI GO 4 app. Upon completion, the drone will
automatically return to its starting point and land.
Figure 4. UAV flight planning
4.5. Data collection
To prepare the images for the training model, data collection was conducted in two stages using a
drone equipped with cameras. The first stage involved collecting images of trees with symptoms on their
7. Int J Elec & Comp Eng ISSN: 2088-8708
Optimizing olive disease classification through transfer learning with unmanned aerial … (El Mehdi Raouhi)
897
fruit, leaves, or bark, such as anthracnose, fumigina, or knot, at a low altitude. The second stage involved
collecting images of trees with symptoms on their foliage, such as verticilliose, at a higher altitude using
downward vision and infrared sensors. The drone flight was conducted on a clear, windless day, and the
flight height was set at 20 m with a 70% overlapping rate for the photos taken. To allow the MobileNet-
transfer learning model to efficiently learn the olive spectral features of tree diseases in visible images, the
olive images were manually annotated.
4.6. Data preprocessing
First, the data pre-processing task starts with a span of data points. Second, implement a data split,
80% for training and 20% for validating. Then, the data augmentation method was applied to improve the
distribution of pixels at various intensities. In complement, low spatial resolution UAV images frequently
have low contrast, poor texture, and minimal edge information. So many critical traits are typically lost after
a sequence of convolution and amplification procedures. To address these challenges, the authors propose an
image reconstruction technique. The method serves to improve the spatial resolution of UAV olive imagery,
resulting in clearer edge contours, best contrast, and enhanced textures to better preserve canopy edge
information.
4.7. Data augmentation
The issue of overfitting during the training process phase of CNN can be overcome. Different data
augmentation techniques are used in this step, including transformations like rotating, flips and intensity
perturbations. In addition, Gaussian noise processing operations are also used. So, the data enrichment
process is done through fine-tuning. By generating additional data through data augmentation as presented in
Figure 5 with imbalanced dataset distribution in Figure 5(a) and balanced dataset distribution in Figure 5(b),
the model is exposed to more variations in the data. Implementing this can aid in mitigating overfitting, a
situation where the model becomes overly adapted to the training data and exhibits poor performance on
unseen data. In an imbalanced dataset, the minority classes may be underrepresented, and the classifier may
tend to predict the majority class more often, resulting in poor performance for the minority classes. To
overcome this issue, several techniques can be used, such as resampling the data to balance the classes, using
different methods to class imbalance, or modifying the learning algorithm to give more weight to the
minority class. In this case study after applying data augmentation techniques, the dataset became balanced
with 2,000 images per class, resulting in a total of 14,000 images.
(a) (b)
Figure 5. Olive diseases dataset distribution (a) before data augmentation and (b) after data augmentation
4.8. Deep transfer learning model
Various algorithms have been employed to classify and detect plant diseases, the exception of
MobileNet architecture was demonstrated on our initial research and are best suited on mobile devices. This
CNN model combined with a transfer learning algorithm in image classification proves beneficial as it
leverages existing knowledge and training for image classification tasks, resulting in more efficient outcomes
compared to training from scratch. In addition, the initial research studies conducted on was very helpful to
support contribution, perspective research and use of new architectures to enhance the olive diseases
classification system. Overall, transfer learning with MobileNet architecture involves using a pre-trained
MobileNet model as a feature extractor, adding new trainable layers, and fine-tuning the model on a new
dataset. The key benefit of transfer learning is that it can significantly reduce the amount of data required to
train a model while improving the model’s performance.
8. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 891-903
898
5. RESULT AND DISCUSSION
5.1. Results
Accuracy and training loss per training period are shown below. As we are dealing with hundreds of
thousands of observations, it is not uncommon to see a neural network model converge rapidly. In this case,
the batches contain 64 observations. In each era, the model will be exposed to more than 5,000 different lots.
Few epochs can be enough to lead to high accuracy and low levels of loss from the start of the training
session. In this exercise, 10 epochs were planned, as the authors were interested in studying potential
overfitting. In a final implementation, a shorter formation is considered and shown in Figure 6 with training
loss in Figure 6(a), training accuracy in Figure 6(b), validation loss in Figure 6(c) and validation accuracy in
Figure 6(d). Also, the Table 3 shows the performance of several convolutional neural network models trained
to classify five different classes of olive diseases: healthy, anthracnose, cyclonium, fumigina, and
verticilliose.
The following metrics are provided for each model: training accuracy, loss and validation accuracy,
loss. The training loss measures the error of the model during training, while the validation loss measures the
error on a separate validation set. The training accuracy and validation accuracy measure the percentage of
correctly classified samples during training and on the validation set, respectively. Based on the table, the
performance of the models varies significantly across the different disease classes. For the healthy class, all
models achieved high accuracy and low loss on both training and validation sets. The MobileNet-TL model
achieved perfect training accuracy on this class. For the other classes, the performance of the models varied.
For anthracnose, the MobileNet-TL model achieved the highest accuracy and lowest loss, while the
EfficientNetB7 model performed the worst. For cyclonium, the MobileNet-TL and DenseNet models
achieved the optimal accuracy and loss, while the EfficientNetB7 model performed the worst. For fumigina
and verticilliose, the MobileNet-TL model again achieved the optimal accuracy and loss, while the ResNet50
model performed the worst. As a reminder, the columns in the confusion matrices correspond to the predicted
classes, whereas the rows relate to the actual classes.
(a) (b)
(c) (d)
Figure 6. Accuracy and Loss by CNN architectures (a) training loss, (b) training accuracy, (c) validation loss,
and (d) validation accuracy
9. Int J Elec & Comp Eng ISSN: 2088-8708
Optimizing olive disease classification through transfer learning with unmanned aerial … (El Mehdi Raouhi)
899
Table 3. The performance evaluation outcomes of CNN models based on the utilized olive disease classes
Olive disease classes CNN model Training loss Training accuracy Validation loss Validation accuracy
Healthy ConvNet 0.5394 0.9479 0.5399 0.8958
EfficientNetB7 0.9377 0.6513 1.4931 0.8125
InceptionV3 0.1991 0.8620 0.3648 0.8750
MobileNet-TL 0.0327 1.0000 0.1458 0.9987
DenseNet 0.2038 0.9115 0.2531 0.8750
VGG19 0.2048 0.8281 0.3570 0.8750
ResNet50 0.4667 0.7943 0.4113 0.8542
VGG16 0.2796 0.8516 0.4553 0.8125
Anthracnose ConvNet 0.1809 0.8802 0.3211 0.8333
EfficientNetB7 0.9578 0.8208 1.9992 0.1875
InceptionV3 0.4050 0.9587 0.1985 0.9167
MobileNet-TL 0.0664 1.0000 0.0168 1.0000
DenseNet 0.2370 0.9375 0.0996 0.9583
VGG19 0.4084 0.9225 0.1306 0.9792
ResNet50 0.5161 0.7943 0.5342 0.7500
VGG16 0.4037 0.8516 0.1562 0.9792
Cyclonium (OP) ConvNet 0.5352 0.9141 0.6262 0.8542
EfficientNetB7 0.2928 0.8450 0.2947 0.8333
InceptionV3 0.2192 0.9193 0.2067 0.9375
MobileNet-TL 0.0257 1.0000 0.0169 0.9988
DenseNet 0.0269 1.0000 0.1443 0.9583
VGG19 0.2267 0.9219 0.1707 0.9792
ResNet50 0.4752 0.8090 0.3995 0.8333
VGG16 0.1908 0.9089 0.2862 0.8542
Fumigina ConvNet 0.1479 0.9507 0.2802 0.8542
EfficientNetB7 0.16012 0.8906 0.2857 0.8333
InceptionV3 0.1534 0.9479 0.1310 0.9375
MobileNet-TL 0.0241 1.0000 0.0458 0.9956
DenseNet 0.1228 0.9609 0.2039 0.9167
VGG19 0.3500 0.8411 0.3091 0.8542
ResNet50 1.8556 0.9245 2.9247 0.7917
VGG16 0.2581 0.9010 0.2619 0..8958
Verticilliose ConvNet 0.1479 0.9507 0.2802 0.8542
EfficientNetB7 0.1602 0.8906 0.2857 0.8333
InceptionV3 0.1534 0.9479 0.1310 0.9375
MobileNet-TL 0.0241 1.0000 0.0458 0.9925
DenseNet 0.1228 0.9609 0.2039 0.9167
VGG19 0.3500 0.8411 0.3091 0.8542
ResNet50 1.8556 0.9245 2.9247 0.7917
VGG16 0.2581 0.9010 0.2619 0..8958
Knot ConvNet 0.1479 0.9507 0.2802 0.8542
EfficientNetB7 0.1602 0.8906 0.2857 0.8333
InceptionV3 0.1534 0.9479 0.1310 0.9375
MobileNet-TL 0.0241 1.0000 0.0458 0.9952
DenseNet 0.1228 0.9609 0.2039 0.9167
VGG19 0.3500 0.8411 0.3091 0.8542
ResNet50 1.8556 0.9245 2.9247 0.7917
VGG16 0.2581 0.9010 0.2619 0..8958
Saisetia Oleae ConvNet 0.1586 0.9543 0.2823 0.8842
EfficientNetB7 0.1602 0.8906 0.2862 0.8323
InceptionV3 0.1534 0.9479 0.1310 0.9375
MobileNet-TL 0.0344 1.0000 0.0248 0.9952
DenseNet 0.1228 0.9703 0.1933 0.9167
VGG19 0.3543 0.8411 0.3091 0.8533
ResNet50 0.3322 0.9245 2.9247 0.7917
VGG16 0.2524 0.9024 0.2613 0.8837
Figure 6 shows the performance results for various CNN architectures with training loss in
Figure 6(a), training accuracy in Figure 6(b), validation loss in Figure 6(c) and validation accuracy in
Figure 6(d). The included CNN architectures are ConvNet, EfficientNetb7, InceptionV3, MobileNet,
DenseNet, VGG19, ResNet50 and VGG16. Also, Figure 7 shows the confusion matrix, which provides
actual and predicted values for each class of olive diseases, respectively: healthy, anthracnose, cyclonium
(OP), fumigina, verticiliose, knot, and saisetea oleae. In classification problems, the model’s overall
performance can be assessed through various metrics. Along with the traditional calculation of statistical
performance measures directly from “predicted” and “actual” test tensors. In addition, confusion matrices
provide a visual and quantitative assessment of model performance on both an aggregate and “per-class”
basis. So, the performances are evaluated here with statistical metrics and confusion matrices for the
MobileNet-TL model. The compared result between classes illustrated in the confusion matrix presented in
10. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 891-903
900
Figure 7 show that some classes present better results than the ones in example class 1, 2, 4 and 5 are best
suited for using transfer learning than 3,6 and 7. This will be discussed in the next section to get more
explanations.
Figure 7. Confusion matrix of ODD classification for MobileNet-TL
The ROC curve presented in Figure 8 depicts the performance of the MobileNet-TL model in
detecting anthracnose disease, which is just one of the seven classes studied. The results clearly indicate that
the MobileNet-TL model demonstrated superior accuracy compared to the other models. However, a more
detailed analysis and explanation of the findings will be provided in the subsequent discussion section.
Figure 8. Receiver operator characteristic (ROC) curve by anthracnose disease class
11. Int J Elec & Comp Eng ISSN: 2088-8708
Optimizing olive disease classification through transfer learning with unmanned aerial … (El Mehdi Raouhi)
901
5.2. Discussion
The findings of this research, focusing on deep learning and the classification of different olive
diseases using drone-collected images, are highly noteworthy, particularly from the perspective of
data science education. The results confirm that the combination of CNN and transfer learning methods
yields highly accurate remote detection and classification of olive diseases. The importance of data
pre-processing is emphasized, as it significantly influences the results. Furthermore, the study demonstrates
the effectiveness of transfer learning, although the selection of an appropriate number of trees and
depth parameters can have a notable impact on the results. The authors experimented with lower tree counts
and shallow depth architectures, which led to some degradation in performance, as anticipated. Nevertheless,
when appropriately manipulated, transfer learning proves to be an excellent predictive tool, with performance
on par with more complex and sophisticated algorithms. In addition, The MobileNet-transfer learning-
based model outperformed CNN alone, as evident from the confusion matrices. This highlights the
importance of selecting the right architecture and hyperparameters. Some potential improvements, such as
handling asymmetric features, cross-validation, and hyperparameter optimization, were left unexplored but
may be revisited in future research to further enhance performance. The primary aim of this research is to
initiate a series of research endeavors geared towards enhancing drone utilization and democratizing drone
technology for small and medium-sized olive farms. Upon analyzing the confusion matrices, specific patterns
emerge, notably a higher level of confusion between classes 3 and 6, indicating a substantial number of
incorrect predictions. Moreover, the overall accuracies by crop class remained consistently high, surpassing
97%, except for “class 7,” which exhibited relatively lower accuracies due to its limited representation in the
dataset. Remarkably, deep learning demonstrated superior performance compared to other methods in
predicting broadleaf classes, underscoring its effectiveness in addressing challenges related to poorly
represented classes. These findings hold significant implications for the future development and application
of UAV-enabled olive disease classification systems. Transfer learning, involving the use of a pre-trained
model on a large dataset as a starting point for a new task or dataset, is a valuable technique. In the context of
mobile architecture, transfer learning enhances model accuracy by leveraging knowledge gained from
pre-training on a large dataset. Two common approaches to transfer learning with mobile architecture are
using the pre-trained model as a feature extractor and finetuning the model. The former extracts feature from
input data using the pre-trained model, which are then fed into a smaller model trained specifically for the
task. The latter approach fine-tunes the pre-trained model’s weights on the specific task, allowing it to adapt
its learned features further. The research results obtained from UAV imagery, as outlined in the
results section, outperform similar and related works in terms of accuracy, enhancing the contribution of
transfer learning in the case study of multi-spectral images of olive trees. The achieved average accuracy of
99% for the seven studied classes demonstrates the potential for improving and optimizing olive crop
production.
6. CONCLUSION AND FUTURE SCOPE
In conclusion, the authors try to address the classification problem of olive diseases, based on
transfer learning techniques and UAV imagery, in order to cover larger areas of olive cultivation and more
disease types. The data collected in this study were aerial and lateral images of olive trees acquired via
cameras in low and high-altitude drone flight, in the study area of Béni Mellal-Khénifra region of Morocco at
different growth stages on the base of seven classes, six of them sick and the seventh healthy. The purpose of
the study was to identify the olive disease classes with minimum of cost and maximum of efficiency and
precision. The proposed MobileNet-TL model based on CNN architecture and transfer learning methods
obtained an accuracy of 99% overcoming the limitations of random sampling methods, overfitting, and
capabilities to deploy the application on mobile devices, also the results is higher than similar research works
presented previously, in terms of accuracy, data volume and types of diseases. To expand this work in the
future, it is recommended to design an integrated intelligent smart agriculture system based on microservices
architecture using UAV classification service combined with smart irrigation service, the aim will be to adapt
the classification system to different environmental variables in order to improve the learning capacities of
the AI system applied to olive crop and more particularly to the early detection and classification of olive tree
diseases.
REFERENCES
[1] V. Lohchab, M. Kumar, G. Suryan, V. Gautam, and R. K. Das, “A review of IoT based smart farm monitoring,” in 2018 Second
International Conference on Inventive Communication and Computational Technologies (ICICCT), Apr. 2018, pp. 1620–1625,
doi: 10.1109/ICICCT.2018.8473337.
[2] R. Chin, C. Catal, and A. Kassahun, “Plant disease detection using drones in precision agriculture,” Precision Agriculture,
vol. 24, no. 5, pp. 1663–1682, Oct. 2023, doi: 10.1007/s11119-023-10014-y.
12. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 891-903
902
[3] H. Alshammari, K. Gasmi, I. Ben Ltaifa, M. Krichen, L. Ben Ammar, and M. A. Mahmood, “Olive disease classification based on
vision transformer and CNN models,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–10, Jul. 2022, doi:
10.1155/2022/3998193.
[4] A. Bouguettaya, H. Zarzour, A. Kechida, and A. M. Taberkit, “A survey on deep learning-based identification of plant and crop
diseases from UAV-based aerial images,” Cluster Computing, vol. 26, no. 2, pp. 1297–1317, Apr. 2023,
doi: 10.1007/s10586-022-03627-x.
[5] A. Šiljeg et al., “GEOBIA and vegetation indices in extracting olive tree canopies based on very high-resolution UAV
multispectral imagery,” Applied Sciences, vol. 13, no. 2, Jan. 2023, doi: 10.3390/app13020739.
[6] N. A. Sehree and A. M. Khidhir, “Olive trees cases classification based on deep convolutional neural network from unmanned
aerial vehicle imagery,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 27, no. 1,
pp. 92–101, Jul. 2022, doi: 10.11591/ijeecs.v27.i1.pp92-101.
[7] A. Di Nisio, F. Adamo, G. Acciani, and F. Attivissimo, “Fast detection of olive trees affected by Xylella fastidiosa from UAVs
using multispectral imaging,” Sensors, vol. 20, no. 17, Aug. 2020, doi: 10.3390/s20174915.
[8] A. Castrignanò et al., “Semi-automatic method for early detection of xylella fastidiosa in olive trees using UAV multispectral
imagery and geostatistical-discriminant analysis,” Remote Sensing, vol. 13, no. 1, Dec. 2020, doi: 10.3390/rs13010014.
[9] A. Safonova, E. Guirado, Y. Maglinets, D. Alcaraz-Segura, and S. Tabik, “Olive tree biovolume from UAV multi-resolution
image segmentation with mask R-CNN,” Sensors, vol. 21, no. 5, Feb. 2021, doi: 10.3390/s21051617.
[10] S. Uğuz and N. Uysal, “Classification of olive leaf diseases using deep convolutional neural networks,” Neural Computing and
Applications, vol. 33, no. 9, pp. 4133–4149, May 2021, doi: 10.1007/s00521-020-05235-5.
[11] R. Morgado, P. F. Ribeiro, J. L. Santos, F. Rego, P. Beja, and F. Moreira, “Drivers of irrigated olive grove expansion in
Mediterranean landscapes and associated biodiversity impacts,” Landscape and Urban Planning, vol. 225, Sep. 2022, doi:
10.1016/j.landurbplan.2022.104429.
[12] H. H. Alshammari, A. I. Taloba, and O. R. Shahin, “Identification of olive leaf disease through optimized deep learning
approach,” Alexandria Engineering Journal, vol. 72, pp. 213–224, Jun. 2023, doi: 10.1016/j.aej.2023.03.081.
[13] F. Sabrina, S. Sohail, S. Thakur, S. Azad, and S. Wasimi, “Use of deep learning approach on UAV imagery to detect
mistletoe infestation,” in 2020 IEEE Region 10 Symposium (TENSYMP), 2020, pp. 556–559,
doi: 10.1109/TENSYMP50017.2020.9230971.
[14] J. Barbedo, “A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses,”
Drones, vol. 3, no. 2, Apr. 2019, doi: 10.3390/drones3020040.
[15] M. B. A. Gibril, H. Z. M. Shafri, A. Shanableh, R. Al-Ruzouq, A. Wayayok, and S. J. Hashim, “Deep convolutional neural
network for large-scale date palm tree mapping from UAV-based images,” Remote Sensing, vol. 13, no. 14, Jul. 2021, doi:
10.3390/rs13142787.
[16] N. Ketkar, Deep Learning with Python. Berkeley, CA: Apress, 2017.
[17] L. Li, S. Zhang, and B. Wang, “Plant disease detection and classification by deep learning-a review,” IEEE Access, vol. 9,
pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
[18] J. del Cerro, C. C. Ulloa, A. Barrientos, and J. de León Rivas, “Unmanned aerial vehicles in agriculture: a survey,” Agronomy,
vol. 11, no. 2, Jan. 2021, doi: 10.3390/agronomy11020203.
[19] C. Bi, J. Wang, Y. Duan, B. Fu, J.-R. Kang, and Y. Shi, “MobileNet based apple leaf diseases identification,” Mobile Networks
and Applications, vol. 27, no. 1, pp. 172–180, Feb. 2022, doi: 10.1007/s11036-020-01640-1.
[20] T. A. Prasetyo, V. L. Desrony, H. F. Panjaitan, R. Sianipar, and Y. Pratama, “Corn plant disease classification based on leaf using
residual networks-9 architecture,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 3,
pp. 2908–2920, Jun. 2023, doi: 10.11591/ijece.v13i3.pp2908-2920.
[21] D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, “PlantDoc,” in Proceedings of the 7th ACM IKDD CoDS and 25th
COMAD, Jan. 2020, pp. 249–253, doi: 10.1145/3371158.3371196.
[22] S. Jadon, “SSM-net for plants disease identification in low data regime,” in 2020 IEEE/ITU International Conference on Artificial
Intelligence for Good (AI4G), Sep. 2020, pp. 158–163, doi: 10.1109/AI4G50087.2020.9311073.
[23] L. M. Tassis, J. E. T. de Souza, and R. A. Krohling, “A deep learning approach combining instance and semantic segmentation to
identify diseases and pests of coffee leaves from in-field images,” Computers and Electronics in Agriculture, vol. 186, Jul. 2021,
doi: 10.1016/j.compag.2021.106191.
[24] M. Long, J. Wang, G. Ding, D. Shen, and Q. Yang, “Transfer learning with graph co-regularization,” Proceedings of the AAAI
Conference on Artificial Intelligence, vol. 26, no. 1, pp. 1033–1039, Sep. 2021, doi: 10.1609/aaai.v26i1.8290.
[25] X. Li, Y. Grandvalet, and F. Davoine, “A baseline regularization scheme for transfer learning with convolutional neural
networks,” Pattern Recognition, vol. 98, Feb. 2020, doi: 10.1016/j.patcog.2019.107049.
[26] A. Ksibi, M. Ayadi, B. O. Soufiene, M. M. Jamjoom, and Z. Ullah, “MobiRes-Net: a hybrid deep learning model for detecting
and classifying olive leaf diseases,” Applied Sciences, vol. 12, no. 20, Oct. 2022, doi: 10.3390/app122010278.
[27] S. Uğuz, “Automatic olive peacock spot disease recognition system development by using single shot detector,” Sakarya
University Journal of Computer and Information Sciences, Sep. 2020, doi: 10.35377/saucis.vi.755269.
[28] M. Alruwaili, S. Alanazi, S. Abd, and A. Shehab, “An efficient deep learning model for olive diseases detection,”
International Journal of Advanced Computer Science and Applications, vol. 10, no. 8, 2019,
doi: 10.14569/IJACSA.2019.0100863.
[29] M. Milicevic, K. Zubrinic, I. Grbavac, and I. Obradovic, “Application of deep learning architectures for accurate detection of
olive tree flowering phenophase,” Remote Sensing, vol. 12, no. 13, Jul. 2020, doi: 10.3390/rs12132120.
[30] J. M. Jurado, L. Ortega, J. J. Cubillas, and F. R. Feito, “Multispectral mapping on 3D models and multi-temporal monitoring for
individual characterization of olive trees,” Remote Sensing, vol. 12, no. 7, Mar. 2020, doi: 10.3390/rs12071106.
[31] P. Rallo et al., “Exploring UAV-imagery to support genotype selection in olive breeding programs,” Scientia Horticulturae,
vol. 273, Nov. 2020, doi: 10.1016/j.scienta.2020.109615.
[32] K. Neupane and F. Baysal-Gurel, “Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: a
review,” Remote Sensing, vol. 13, no. 19, Sep. 2021, doi: 10.3390/rs13193841.
[33] A. El Orche et al., “Evaluation of the capability of horizontal ATR-FTMIR and UV-visible spectroscopy in the discrimination of
virgin olive oils from the moroccan region of Beni Mellal-Khenifra,” Journal of Spectroscopy, vol. 2020, pp. 1–9, Jun. 2020, doi:
10.1155/2020/9317350.
13. Int J Elec & Comp Eng ISSN: 2088-8708
Optimizing olive disease classification through transfer learning with unmanned aerial … (El Mehdi Raouhi)
903
BIOGRAPHIES OF AUTHORS
El Mehdi Raouhi received the bachelor’s degree in mathematics and computer
science from Mohamed V University, Morocco, in 2008 and the M.S. degree in software
quality from Ibn Tofail University, Morocco in 2010. Currently, he is A Ph.D. student at LTI
Laboratory, ENSA, University Chouaib Doukkali, El Jadida, Morocco. His research interests
are in smart farming based on a system of prediction and aid in the diagnosis of plant diseases
in agriculture. He can be contacted at email: raouhi@gmail.com.
Mohamed Lachgar he obtained his Ph.D. in computer science from Cadi Ayyad
University in 2017. Currently, he serves as a professor of computer science at the National
School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco. His research
interests encompass a broad spectrum, including automation tools development in embedded
software, software modeling and design, metamodel design, model transformation, model
verification and validation methods, machine learning, and deep learning. He can be contacted
at email: lachgar.m@gmail.com.
Hamid Hrimech in 2009, he received a Ph.D. in computer science from Arts et
Métiers ParisTech in France. He is an associate professor at University Hassan 1st at
Morocco’s ENSA Department of Computer Science and Mathematics. Artificial intelligence,
collaborative interactions in a collaborative virtual environment and driving simulation are
among his research interests. He can be contacted at email: hrimech@hotmail.fr.
Ali Kartit (November 2011) after completing his Ph.D. in computer science,
specializing in computer network security, he earned his degree from Rabat’s Faculty
Mohamed V University. Presently, he serves as an assistant professor at University Chouaib
Doukkali. The author has a wealth of computer expertise, spanning over 14 years, with a
decade dedicated to technical and vocational education as a computer network trainer,
particularly in the management module “computer network security principles”. He can be
contacted at email: alikartit@gmail.com.