1. Artificial Intelligence : An Advanced
Technological Innovation in Agriculture
Speaker: Sayanika Ghosh, M.sc (Ag.), 3rd semester
Department of Agronomy
Faculty of Agriculture
Bidhan Chandra Krishi Viswavidyalaya
Mohanpur, Nadia, 741252
Seminar Committee:
Prof. Pintoo Bandopadhyay
Prof. Champak Kumar Kundu
Dr. Ratneswar Poddar
Chairman: Dr. Sunil Kumar Gunri
Course No. AGRON 649 Seminar - I Date: 10.01.2023

2. Indian Agriculture :- At a Glance
 Agriculture is the backbone of Indian Economy. Agriculture sector in India holds the record for
second-largest agricultural land in the world generating employment for about half (49%) of the
country’s population.
 Net cropped area 141 million hectare with 48% irrigation facilities.
 This sector is important source of food for a large majority of people.
 Two main agriculture season i.e. Kharif and Rabi Season.
 India has the world's largest area planted to wheat, rice, and cotton, and is the largest producer of
pulses and spices in the world.
Source : FAO, 2021

3. Productivity Vs India
 Although India has attained self-sufficiency in food staples, the productivity of its farms is below
that of Brazil, the United States, France, and other nations. Indian wheat farms, for example,
produce about a third of the wheat per hectare per year compared to farms in France.
 Rice productivity in India is less than half that of China. Other staple’s productivity in India is
similarly low.
 Indian total factor productivity growth remains below 2% per annum; in contrast, China’s total
factor productivity growth is about 6% per annum, even though China also has small holding
farmers.
 Several studies suggest India could eradicate its hunger and malnutrition and be a major source of
food for the world by achieving productivity comparable with other countries.
Source: Goyal et al., 2016

4.  In farming, different weather factors such as Rainfall, temperature, and humidity play an
important role. Due to pollution, sometimes climate varies abruptly, and hence it becomes
difficult for farmers to make proper decisions for soil preparing, sowing seeds and harvesting.
 For a better crop, it is necessary that the soil should be productive and have the required
nutrition, such as Nitrogen, Phosphorous, and Potassium. If these nutrients are not present in
effective way in the soil, then it may lead to poor quality crops. But it is difficult to identify these
soil-quality with traditional ways.
 In the agriculture lifecycle, it is required that we save our crops from weeds. Else it may
increase the production cost, and it also absorbs nutrients from the soil. But by traditional ways,
identification and prevention of crop from weeds is not efficient.
Challenges with Traditional Agriculture

5. Solution? Artificial
Intelligence

6. Artificial Intelligence
Artificial Intelligence (AI) is
the intelligence exhibited by
machines, rather than human
or other animals. The
intelligent agents perceives
its environment and takes
action to maximise the
success (Russell & Norvig,
2003)

7.  The concept of AI is invented by Herbert Simon (1965) and the word ‘Artificial
Intelligence’ is coined by John McCarthy.
 It deals with the simulation of intelligent behaviour in computer.
 It is the capability of a machine to imitate intelligent human behaviour.
 It performs tasks that normally require human intelligence, such as visual
perception, speech recognition, decision making and translation between
languages.
 It is not “Man Vs Machine” but is “Man and Machine” synergy.
Artificial Intelligence

10.  Artificial Intelligence is transforming the agriculture industry. Today's agriculture
system has reached at a different level due to AI.
 Saves the agriculture sector from different factors such as climate change,
population growth, employment issues in this field and food safety.
 Artificial Intelligence has improved crop production and real-time monitoring,
harvesting, processing and marketing.
 Different hi-tech computer-based systems are designed to determine various
important parameters such as weed detection, yield detection, crop quality and
many more.
Artificial Intelligence in Agriculture

11. Benefits of AI in Agriculture
 Optimises the resource use and efficiency.
 Solves the scarcity of resources and labour to a large extent.
 Technological revolution and boon in agriculture to feed the
increasing human population of world.
 Complement and challenge to make right decision by farmers.

12. Opportunities of AI in Agriculture
Global Food Demand
Availability and Affordability of
Infrastructures for Data Collection
Competitiveness and Increasing
Cost of Labour and Materials
International/Governmental
Initiatives for AI in Agriculture

13. Operations which can use AI in Agriculture
Collecting Data during different Crop Growth Stages
Testing of Soil
Image based Insight Generation
Disease Detection and Diagnosis
Agri Products Marketing
Spraying of Pesticides and Herbicides
Tackling the Labours Shortage

15. Automated Irrigation System
 Automated irrigation systems can be monitored using
a Smartphone or Laptop.
 The device is connected with sensors that collect data
like moisture level, crop status, temperature, RH, soil
status, etc. and transmit that data to the farm
irrigation system.
 As soon as the deficiency is predicted the device sends
the alert and the water sprinkler is turned on.
 This helps to facilitate irrigation scheduling and
improve water use efficiency.

16. Components used in Automated Irrigation System
Moisture sensors Water Pump Automated water
Pump Controller
Solenoid valves
Metering Pump Relay Module Sprinkler

17. How Automated Irrigated System Works ?
Start
Received signals from soil
moisture sensors
Compare the
values from the
moisture sensor
with desired set
Start Irrigation End
If soil is wet
If soil is dry

18. Closed loop
system
• based on a
predefined
irrigation scheme
the control system
takes over and
makes detailed
decisions on when
and how much
water to apply.
Open loop
system
• based on the
amount of water to
be applied and the
timing of the
irrigation.
Volume
based system
• based on a pre-set
amount of water
that can be applied
in the field.
Time based
system
• works with time
clock controllers.
Types of Automated Irrigation System

19. Prospero or The Swarming Farmbot
• Prototype of an Autonomous Micro Planter
(AMP).
• Plant, tend, and harvest autonomously
transitioning from one phase to another.
• Has the ability to farm inch by inch, examining the
soil before planting each seed and choosing the
best variety for that spot.
• Can maximize the productivity of each acre

20. Strawberry Harvesting Robot
• Harvest the strawberry quite
easily by minimizing the
shortage of farm labour at
the peak time ensuring
maximum production,
ultimately helping to tamp
down price growth.

21. TADD
 The Trainable Anomaly Detection and Diagnosis
System (TADD) can detect, identify, and quantify many
of the common blemishes affecting potatoes (Pang et
al., 2021).
 Currently only a vision system, but even spotting an
off- colour spud is more difficult than it appears.
When potatoes are exposed to too much light, they
turn green, but the greening of red potato varieties
appears black.
 Also it can lower labour input and create a possibility
for higher food safety assurance.

22. Drones for Spraying
 The device is far superior to manual
spraying because it is much faster and
ensures uniform and judicious
spraying of agricultural inputs across
the entire crop.
 As drones do not contact the soil,
they can help to avoid crop damage
caused by compaction.

23. Implementation Details Components Controller Nozzle Remarks Load Author
Android device is implemented
to the quad copter. These
android applications are used to
control the quad copter for
spraying pesticides and fertilizer.
IMU , barometer,
accelerometer,
gyroscope.
Arduino board - Reduces the health
issues to face the
farmers during spraying
pesticides and fertilizer.
- Shilpa Kedari et
al. (2016)
Tracking the precise location
where the air pollution is
remarkable with GPS module
ESC, BLDC motor,
sensor such as LM35,
AM1001, LDR, MQ6,
and MQ135.
Arduino Uno
ATmega328
- It is low cost and better
efficient model.
- Munmun
Ghosal et
al.(2018)
Develop the droplet density and
size of the hexa copter mounted
sprayer
Camera, Gyro, GPS,
BLDC, ESC.
Flight Controller Flat fan It is used to spraying
the crops without
human intervention.
5 L Yallappa D et
al.(2018)
Study the impact of the spraying
system in different height and
different sprayer
GPS, digital
temperature,
humidity indicator,
water sensitive
N-3 type Rotary
atomizer
In this UAV was firstly
used for low altitude
and low volume
pesticides application
25 L Weicai Qin et al
(2019)
Different Components Mounted in Drones and their Uses

25. Intercomparision of Drone and Conventional Spraying Nutrients on Crop Growth
and Yield in Black Gram
P. Nandhini a , D. Muthumanickam a , R. S. Pazhanivelan b , R. Kumaraperumal a , K. P. Ragunath b and N. S. Sudarmanian
Treatment Treatment Details
T1 Battery operated Drone spray of 1% All 19 with jet type nozzle and a spray fluid of 25 L ha-1
T2 Fuel operated Drone spray of 1% All 19 with jet type nozzle and a spray fluid of 25 L ha-1
T3 Fuel operated Drone spray of 1% All 19 with Atomizer nozzle and a spray fluid of 75 L ha-1
T4 Manual spray of 1% All 19 with knapsack sprayer and a spray fluid of 500 L ha-1
T5 Battery operated Drone spray of 1% TNAU pulse wonder with jet type nozzle and a spray fluid of 25 L ha-1
T6 Fuel operated Drone spray of 1% TNAU pulse wonder with jet type nozzle and a spray fluid of 25 L ha-1
T7 Fuel operated Drone spray of 1% TNAU pulse wonder with Atomizer nozzle and a spray fluid of 75 L ha-1
T8 Battery operated Drone spray of 2% TNAU pulse wonder with jet type nozzle and a spray fluid of 25 L ha-1
T9 Fuel operated Drone spray of 2% TNAU pulse wonder with jet type nozzle and a spray fluid of 25 L ha-1
T10 Fuel operated Drone spray of 2% TNAU pulse wonder with Atomizer nozzle and a spray fluid of 75 L ha-1
T11 Manual spray of 2% All 19 with knapsack sprayer and a spray fluid of 500 L ha-1
T12 Control (Water spray)

26. Treatment No. of pods plant-1 1000 seed weight Haulm Yield (Kg ha-1) Grain Yield (Kg ha-1)
T1 11.4 3.82 1094 565
T2 11.6 3.88 1118 578
T3 11.9 3.98 1130 593
T4 9.3 3.65 904 414
T5 13.0 4.10 1225 641
T6 13.3 4.16 1277 656
T7 13.6 4.28 1313 682
T8 14.9 4.46 1412 733
T9 15.2 4.53 1446 737
T10 15.8 4.67 1525 784
T11 10.4 3.73 999 500
T12 8.2 3.60 811 313
SE.d 0.46 3.82 24.5 18.6
CD (0.05) 0.96 3.88 50.9 38.6
Effect of Foliar Spray on Yield attributes and yield of Black gram

27. • Using hyperspectral imaging,
spectroscopy and 3D mapping allows for
the substantial increase in the number of
physical observables in the field.
• The multi sensor collection approach
creates a virtual world of phenotype data
in which all the crop observables become
mathematical values.
Remote Sensing-based Crop Health
Monitoring

28.  Blue River Technology is used
for thinning and weeding to
increase yield.
 Advanced Artificial Intelligence
Algorithms to make plant-by-
plant decisions to optimize
yield.
Blue River Technology

29. Decision Support System for Greenhouse Tomato
Production
 The key parameters that affect the quality and quantity of
tomatoes in the greenhouse are temperature andCO2.
 Air temperature (day and night), fruit temperature,
radiation, CO2 concentration, fruit load, plant density,
stress, and other environmental factors all have an impact
on tomato plant growth and productivity.
 A decision support system adjusts the temperature
dynamically during the ripening process to improve yield.

30. • The system enables the modeling of environmental
factors such as day and night air temperature, fruit
temperature, radiation, CO2 concentration, fruit
load, plant density, stress etc. that influence
crop growth and production.
• Actuators are controlled by set points, and sensors
provide feedback on measured data to the control
loop. Algorithms for automated greenhouse
climate control have already been developed for
decades.
Greenhouse Based Climate Controller With
Ai-based Techniques

31. GIGAS : Guelph Intelligent Greenhouse
Automation System
The robotic arm is equipped with visioning
technology that allows it to identify ripe produce.
It also has a special gripper that allows it to pluck
fruit from vines without damaging it.
The robot can detect symptoms that humans cannot
yet see (Moussa and Abdullah, 2012).
While patrolling the greenhouse, the robot collects
data and assesses whether a plant is growing well or
getting enough water, for example.

32.  Coupled with some sensors, radar,
and GIS systems and can be
controlled through a controlling
device.
 Used for tillage operations as well as
for a few other operations in
agriculture.
 Automated capabilities, it is both
efficient in precision agriculture and
meets one of the challenges around
labour shortages.
Automated Tractor (Driverless Tractor)

33. Technical Features of Automated Tractor
 This tractor has state- of- the-art technology and the features of this tractors are mentioned below :
 It has an Auto-steer technology, which is based on GPS and this is the one which helps the tractor to travel in a straight
path.
 The features of Auto-headland turn which makes the tractor to turn along the side rows for the operation to be
continuous without giving any input in terms of steering by the operator.
 Auto-implement lift is a feature which will make the tractor lift the took of the work from the land at the end of a row
and the tool will be lowered after the tractor takes a turn to operate the next row.
 Skip-passing is another feature in the driverless tractor will make the tractor to drive to the next row for the operation
to be continuous without any sort of interruption from the operator.
 There would be a Geofence lock which will prevent the tractor from moving away from the boundaries of the farm.
 A Tablet User Interface is used for the control which would help the farmer to provide different inputs to the tractor.
 It has a Remote Engine Start and Stops which would be able to abort the engine which would stop the tractor
completely in the situations of emergency.

34. • 3-feet-by-3-feet, is self-propelled, and uses global
positioning system (GPS).
• Recognize 25 different kinds of weeds and eliminate
them by using its weed-removing attachments.
• Eco-friendly, because it sprays exactly above the
weeds (it can reduce herbicide usage by 75 percent).
• Cheaper than tools currently used for weed-
elimination as it can work during extended periods of
time.
Hortibot Or Weeding Robot

35.  Artificial Intelligence (AI) has been used in a new system to
monitor the oriental fruit fly (Bactrocera dorsalis).
 To predict impending outbreaks, the system employs infrared
beams to detect the number of flies in thearea.
 Built upon the basis of wireless sensor networks and GSM
networks effectively captures long-term and up-to-the-minute
natural environmental fluctuations in fruit farms.
 Two machine learning techniques, self-organizing maps and
support vector machines are incorporated to perform adaptive
learning and automatically issue a warning message to farmers
and government officials via GSM networks.
Autonomous Early Warning System For Fruit Fly

36. Recent Developments in India
 Karnataka government signed MoU with Microsoft India develop Multi-
variant Agricultural Commodity Price Forecasting Model.
 Microsoft with ICRISAT deloyed a Sowing Advisory Service in Kharif season
Under ‘Bhuchetna Project’.
 Microsoft developed Price Forecasting Model in Karnataka.
 IBM providing tools to entrepreneurs/ startups to develop solutions.
 AI-Powered data driven supply chain optimization platform by Matrix
Partners India in New Delhi.

37.  PEAT, Earth Food and V Drone Agro use AI and to assess soil
conditions over the cloud.
 SatSure in India, assess imageries of farms and predict
monetary prospects of their future yield.
 Monsanto had trained the AI algorithms for 15 years which
could predict the corn variety’s highest performance in the first
year trial.
 Syngenta announced ‘AI for Good’ to provide seed genetic
information as well as climate, soil data for suitability of the
variety to an area.

39. 1. Cropin
Founder: Krishna Kumar
Started in: 2010
It provides SaaS-based services to agribusinesses through an
intelligent, self-evolving system that gives farming solutions.
The company uses technologies like big data analytics, artificial
intelligence, and machine learning to create an interconnected
network of stakeholders at different stages of the agricultural
space.
In addition, it provides decision-making tools, live reporting,
analysis, and interpretation mechanisms.

40. 2. DeHaat
Founder: Shashank Kumar
Started in: 2012
DeHaat provides AI-enabled technologies to disrupt the supply chain
and production efficiency and services like distributing high-quality
agricultural inputs and financial services.
With the help of data science, agriscience and machine learning
technologies, it is working on an AI engine that will correlate the
parameters that impact agriculture.
Through predictive analytics, it will provide early warning solutions for
better production and prediction. Presently, the company operates in
Bihar, UP, Odisha, and West Bengal.

41. 3. Aibono
Founders: Vivek Rajkumar
Started in: 2014
It is an AI-powered fresh food aggregator and
brings a “Seed-to-plate” platform.
It synchronizes real-time production with real-
time consumption of super perishable fruits
and vegetables with the help of predictive
analytics, precision farming, and just-in-time
harvests.

42. 4. Ninjacart
Founders: Ashutosh Vikram, Sharath Loganathan, Vasu Devan, Kartheeswaran KK,
Thirukumaran Nagarajan
Started in: 2015
Ninjacart is a business-to-business fresh produce supply chain that connects farmers
and manufacturers to retailers directly.
It aims to solve supply chain problems in Indian agriculture with the use of
technology and data science.
It focuses on problems like food wastage, information barriers, distribution
inefficiency, cash handling, high input cost and low-quality food.
It uses market intelligence tools, machine learning methods to predict market prices,
and deep learning algorithms for forecasting demand (reducing food wastage).

43. Founder: Arvind Godara
Started in: 2016
Agribolo provides farmers with the latest
mandi/weather updates, best farm practices,
expert advice related to soil health and nutrition,
crop prices, a variety of seeds and optimum usage
of fertilizers.
It also provides Agri Mart and Agro Services, which
are marketplaces to buy/rent/sell agri-based
products and services along with e-mandi services.
5. Agribolo

44. Founders: Ananda Verma and Shailendra Tiwari
Started in: 2018
Fasal is an AI-powered platform for the agriculture ecosystem that
records different growing conditions on the farm.
It uses data science and AI algorithms to make on-farm predictions
before delivering the insights anywhere on any device.
It helps in weather forecast at field level, irrigation management,
pest and disease management, fertilizer, fungicide and pesticide
application management and also gives real-time alerts about the
crop, soil, and weather conditions.
6. Fasal

45. Challenges of AI in Agriculture

46. Recommendations
 Adopt a deliberate policy to drive AI innovation, adaptation and proliferation in all
sectors.
 Policymakers should make AI a critical component flagship programmes such as Make
in India, Skill India and Digital India.
 The farmers should seek cognitive technologies (e.g. AI) to maximise return on crops.
 Farmers should be directed towards precision agriculture, AI can be the best tool to
assist it.
 AI should ensure active participation of farmers to cope complexity in modern
agriculture.

47. Conclusion
Is it possible for artificial intelligence (AI) to replace farmers'
traditional knowledge?
 Initially, AI is unlikely to challenge or improve agricultural practices, but in the
near future, it may supplement decisions made by farmers. As a result of such
technological interventions, farmers' lives are likely to improve qualitatively as
well as quantitatively.
 Artificial intelligence helps alleviate labour and resource shortages to a great
extent.
 This tool will be very useful in assisting organizations in dealing with the
increasing complexity of modern agriculture.

48. Reference
Artificial Intelligence in Agriculture. Part 1: How Farming is Going Automated with Robots – AI.Business.
Artificial Intelligence in Agriculture. Part 2: How Farming is Going Automated with AI Technologies –
AI.Business
Agriculture in India UPSC Exam Preparation, Issues In News, Agriculture for UPSC (byjus.com)
CFS: Annual Report 2021 (fao.org)
GOYAL, S. & Prabha, & Rai, Dr & Singh, Shree Ram. (2016). Indian Agriculture and Farmers-Problems and Reforms.
Russell,S. J., & Norvig, P. (2003). Artificial Intelligence:A Modern Approach (Harlow).
Saad, N. M., Noor, N. S. M., Abdullah, A. R., Muda, S., Muda, A. F., & Musa, H. (2017). Segmentation and classification analysis
techniques for stroke based on diffusion weighted images. IAENG International Journalof ComputerScience,44(3), 388-395.
Sasmal, Joydeb. (2016). An Overview of the Indian Agriculture. 10.1007/978-981-10-0895-5_1.
https://www.ubs.com/microsites/nobel-perspectives/en/laureates/herbert-simon.html

49. Name Designation Department Role
Dr. Sunil Kr. Gunri
Associate
Professor
Agronomy Chairperson
Dr. Champak Kr. Kundu Professor Agronomy Member
Dr. Anurup Majumder Professor
Agricultural
Statistics
Member
Dr. Kalyan Jana
Assistant
Professor
Agronomy Member
Advisory Committee