1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 6, November - December (2013), pp. 167-174
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IJCET
©IAEME
IDENTIFICATION OF ARABLE AND TREE CROPS BY EDGE AND
TEXTURE FUSION TECHNIQUES
C. S. Sumathi
Assistant Professor, O/o Dean (SPGS),
TamilNadu Agricultural University, Coimbatore- 641 003, Tamil Nadu, India
A.V. Senthil Kumar
Director, Department of Post Graduate and Research in Computer Applications,
Hindusthan College of Arts and Science, Coimbatore- 641 028, Tamil Nadu, India
ABSTRACT
Crop identification has vast applications in agriculture and medicine. Leaves are classified as
simple or compound leaves by their characteristics. It is important to classify them accurately as they
are important for the country’s economy and environmental protection. This research suggests a new
leaf image based arable and tree crops identification process. Leaves come in many shapes and sizes;
they are often used to help identify plants. Some leaves are flat and wide; others are spiky and thin.
They are made of a single leaf blade connected by a petiole to the stem. Data’s were collected by the
use of digital camera, which are then pre-processed and used. Edge and texture extraction are
through Gabor filter, fusing them for classification. CART and Radial basis function measure
classification accuracy. The experimental result shows this method for classification gives average
accuracy of 88.21% when tested with 195 instances of 6 species of arable and tree crops leaf images.
Index terms: Crop Identification, Leaf Image, Gabor Filter, CART, RBF, Sobel Edge Detector,
Texture, Edge.
I. INTRODUCTION
Plant species identifies individual plants correctly assigning them to descending series of
related groups through common characteristics [1]. Plants are sources for living be it industry, food
or medicine. Arable farming refers to agriculture concerned with cultivation of field crops on fertile
land. The crops can be cereals, vegetables and plants that produce oil or cloth. Arable land is
occupied by crops of both source and harvested during the same agricultural year and sometimes
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
more than once. It usually has a nearby water source and an adequate temperature and requires less
attention than livestock farming. Leaves are basis for identifying plants as they are observed easily.
Tree crops are groves or orchards of tree grown for economic and environmental benefit while fruit
trees are the common types of tree crop. Tree crops may be grown in massive quantities, but popular
for small business and family farms. In many regions tree crops make up a significant portion of
farming community. Fruit trees are typically grown for their seasonal harvest and may live for
decades as producing trees. Leaves on a plant have different sizes and appearance varying in size,
color and shape. Some leaves are unique and easy to identify and some are similar and hard to
classify, as many crops share leaves of similar shape. To securely identify a crop it is necessary to
look at the features of leaves viz., whether it is a toothed or lobed leaves, compound or simple leaves,
texture of the leaves etc.,
Data mining is the process of analyzing data from different perspectives and summarizing it
into useful information. This software is one of a number of analytical tools for analyzing data. It
also allows users to analyze data from many different dimensions and summarize the relationships
identified. Different levels of analysis include Artificial Neural Networks, Genetic Algorithms,
Decision trees, Nearest Neighbor method, Rule induction and Data visualization. Classification,
association rule, clustering etc. are the methodologies to approach the various problems. To classify
the leaves, classification technique is applied to obtain better results. Automatic digital plant
classification and retrieval are by extracting features from leaves [2]. This work identifies crops
based on shape and texture.
Plants play a role in preserving ecology and environment, maintaining atmosphere and
providing sustenance and shelter to insect and animal species [2]. Botanists name a plant to ensure its
unique place in the biological world and to clarify its relationships in that world. Classification is
difficult. As modern botany advances, increasing understanding of biochemical mechanisms, criteria
of plant classification has been transformed. Also, nature is not fixed, and plants are capable to
changes. Despite great advances in botany, there are plants yet to be discovered, classified, and
utilized; unknown plants are treasures awaiting finding. Ethno botanists combine world regions
looking for tomorrow's medicines and food crops. They explore plants functional properties and
relationships in ecosystems to understand the diversity in how we manage plant resources
The plant world is in constant flux. Due to human and other factors, many plants and animals
becoming extinct are possible. Plant classification aids tracking our planet's endangered inhabitants.
Realizing the need to understand ecological systems, preserving biodiversity, scientists are exploring
how genetic diversity and ecological sensitivity are necessary to solve problems like feeding
populations and fighting disease. Plant classification is vital for this. However, there are
disadvantages as some are appropriate to certain species and some methods compare feature
similarity when it is required that the human element enter queries manually [2].
According to Choras [3], texture is a powerful regional descriptor helping retrieval. Texture,
on its own is incapable of finding similar images, but can classify textured images from non-textured
ones and then combine with other visual attributes like shape to ensure effective retrieval. Texture
features are extracted through various methods. Local binary pattern (LBP), gray-level occurrence
matrices (GLCMs) and Gabor Filter are popular feature extraction methods [4, 5]. Most approaches
use global shape features. This research proposes to improve leaf image classification by considering
edge and texture features.
II. LITERATURE REVIEW
A contour signature classification of plant species was proposed by Beghin, et al, [6]. The
shape-based method extracts contour signature from leaves and calculates dissimilarities between
them using a Jeffrey-divergence measure. The orientation of edge gradients analyzes the leaf’s
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3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
macro-texture. This method presents good results for 10 of 18 species which were successfully
classified with classification rates of about 81.1%
A new classification method Move Median Centers [MMC], based on digital morphological
features was proposed by Du, et al., [7]. Plant leaf is acquired through a digital camera and its grey
level image is obtained after preprocessing. Features are extracted from leaf contours. Digital
morphological features include geometrical features and invariable moment features. Instances are
trained with MMC to ensure efficient plant recognition classifier with a recognition rate greater than
75% for every class exceeding three samples. Hence experiment results proved the new method is
effective by comparing 1-NN and k-NN classifier and MMC classifier leading to saving storage
space and reducing classification time.
Lee and Chen [8] constructed a leaf database and proposed region based features
classification. It is robust than contour based features as major curvature points are hard to detect.
The experiment classifies every test image to a single class as all leaf images are real and true classes
known. This method used 3 features, including Centroid Contour Distance (CCD) curve, eccentricity
and Angle Code Histogram (ACH). This method’s effectiveness is demonstrated with experiments
revealing 82.33% classification accuracy for 1-NN rule and recall rate for 10 returned images as
48.27%.
A novel approach for plant classification based on texture properties characterization was
introduced by Rashad, et al., [9]. They used a combined classifier learning vector quantization with
radial basis function. The new system’s ability to classify and recognize a plant from a leaf’s part is
its advantage. Without depending on the leaf’s shape or its color features, it classifies a plant with
only a portion as the new system needs textural features alone. This system is useful for researchers
who identify damaged plants as this is possible from a small available part. This system is applicable
as a combined classifier procedure producing high performance, superior to tested methods with
98.7% correct recognition rate as seen in results.
A method that incorporates shape, vein, color, and texture features was proposed by Kadir, et
al., [10]. They used Probabilistic Neural Networks (PNN) as classifier for plant leaf classification.
Several methods exist for plant leaf classification, but none captured color information as color was
not recognized as an important identification aspect. Here, color plays an important role in
identification. Experiment reveal that the proposed method provides 93.75% average accuracy when
tested on Flavia dataset having 32 plant leaf kinds.
III. METHODOLOGY
Edge detection detects pixels representing the boundary image. Edge orientation, edge
structure and noise environment are variables involved in edge operator selection. Laplacian based
Edge detection, Sobel edge detection, gradient based edge detection and Canny edge detection
methods are common edge detection methods. This research uses Sobel edge detection method with
default threshold is being applied for edge detection. Sobel edge detector is a discrete differentiation
operator using derivative approximation to locate edges. Resulting gradient approximations at every
image point combined to give gradient magnitude represented as
Edge location is declared if gradient value exceeds a threshold. When threshold value
exceeds gradient value it is compared to threshold value and the edge is detected.
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4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
A gradient magnitudes series is created using a simple convolution kernel. The convolution is
mathematically represented as:
1
N ( x, y ) =
1
∑ ∑ K ( j, k ) p ( x − j, y − k )
k =−1 j =−1
For easy computation, Sobel detector uses 2 convolution kernels to detect changes in
horizontal contrast (hy) and vertical contrast (hx). Standard Sobel operators, for a 3×3 neighborhood,
each a simple central gradient estimate is vector sum of a pair of orthogonal vectors [11]. Every
orthogonal vector is a directional derivative estimate multiplied by a unit vector specifying
derivative’s direction. Vector sum of these simple gradient estimates amount to a vector sum of 8
directional derivative vectors.
Pseudo-Codes For Sobel Edge Detection
Input: A Sample Image.
Output: Detected Edges.
Step 1: Accept the input image.
Step 2: Apply mask Gx,Gy to the input image.
Step 3: Apply Sobel edge detection algorithm and the gradient.
Step 4: Masks manipulation of Gx,Gy separately on the input image.
Step 5: Results combined to find the absolute magnitude of the gradient.
Step 6: The absolute magnitude is the output edges.
The Gabor wavelet represents a band-pass linear filter, and harmonic function by a
Gaussian function provides impulse response. Segmenting texture requires partitioning images into
different textured regions accurately. The 2D Gabor filter constitutes a sinusoidal plane of specific
frequency and modulated Gaussian. The approach uses 2D Gabor filter for leaf classification having
form,
where h(x,y) = cos (2πu0x)
represents Gaussian curve directions along x and y axis
respectively and u0 modulation frequency.
This method’s advantage is that arable crops leaves are identified without dependence on
color or whole leaf or its characters as it requires only the arable leaf’s texture and edge. Hence, this
method is used by scientists who identify different crops. Moreover, the new system can be used to
identify crops based on leaves available as this system requires only leaf texture and edge and not
color or other features. Arable crops like neem, guava, sapotta, mango, sunflower which are highly
dynamic in nature were classified with a combined classifier method. Results reveal that the new
method produced high performance compared to other tested methods and hence was applied.
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5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
Flowchart
IV. RESULT AND DISCUSSION
Six species and a total of 195 samples of arable crops were selected. Sample image of the
leaves used is shown in figure 1. The features are extracted using Sobel edge and Gabor filters. The
extracted features are classified using Classification and Regression Trees (CART) and Radial Basis
Kernel (RBF).
Figure 1: Leaf Samples taken from a digital camera
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6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
Matlab was used to extract the edge and texture features and fused. The feature extracted
using Sobel edge detector is shown in figure 2
Figure 2: Edge feature extracted
Gabor filter, used to extract the texture are shown in figure 3 and figure 4 respectively.
Figure 3: Gabor features
Figure 4: DCT representation of frequency domain
Classification accuracy of CART and RBF are given in figure 5, figure 6, figure 7.
Figure 5: Classification accuracy
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7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
Figure 6: Root relative squared error
Figure 7: Root mean squared error
It is observed that the CART achieves the best classification accuracy of 88.21%. Further
investigations are required to improve the classification accuracy.
V. CONCLUSION
This research proposed a fusion of features of arable crops using Gabor filter and Sobel edge
detector for edge based features. CART and RBF classifiers trained and tested extracted features.
The output with CART is feasible with low percentage of incorrectly classified instances and relative
squared error for 195 instances. It can be seen that CART outperforms RBF by improvement in
recall and precision. Further work includes classification of deficit leaves and combining flexible
neural networks to increase the accuracy rate are necessary.
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ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME
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