presentation at the The 11th International Computer Engineering Conference (ICENCO2015) – Cairo, Egypt

1. Tomato leaves diseases detection approach based
on support vector machines
The 11th International Computer Engineering Conference (ICENCO2015) – Cairo, Egypt
Usama Mokhtar
http://www.egyptscience.net
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2. Agenda
 Introduction
 Problem Definition
 Motivation
 Proposed Approach
 Experimental Results
 Conclusion and Future Works
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The 11th International Computer Engineering Conference (2015)

3.  Tomatoes are one of the most widely cultivated food
crops throughout the world due to its high nutritive value.
It contains a lot of vitamins and nutrients such that
vitamin C. It occupies the fourth level between word
vegetables.
 Egypt is one of the famous countries that interested in
tomatoes cultivation. It ranked fifth among leader
countries in the world.
Introduction
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4.  During cultivation process, tomato leaves expose to
many of problems and diseases such as:
 Late blight
 powdery mildew
 Early blight
 Bacterial spot
 Gray mold
Problem Definition
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5. Problem Definition
 The naked eye observation of experts is the main
approach adopted in practice for detection and
identification of plant diseases.
 But this approach requires continuous monitoring of
experts which might be expensive and difficult especially
in large farms.
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The 11th International Computer Engineering Conference (ICENCO2015) – Cairo,

6. Problem Definition
 So, It is necessary to help farmers in automatically detect
symptoms of disease as soon as they appear by
analysing the digital images which may helping us for:
 minimizing major production and economic losses,
 ensuring both quality and quantity of agricultural products and
 minimizing agrochemicals use.
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7. Motivation
 The aims of this research are:
To present a hybrid model that employs gabor wavelet
transform technique to extract relevant features related to
image of tomato leaf along with Support Vector Machines
(SVMs) with alternate kernel functions in order to detect and
identify type of disease that infects tomato plant.
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8. General proposed approach
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9. Image preprocessing phase
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10. Feature extraction phase
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11. Classification phase
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12. Overall structure layout
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13. Experimental Results: Used Dataset
 The datasets used for experiments were constructed based
on real sample images of diseased tomato leaves. Datasets
of total 200 infected tomato leaf images with Powdery mildew
and early blight were used for both training and testing
phase.
 In this approach, SVM is employed using different kernel
functions including Cauchy kernel, Invmult Kernel and
Laplacian Kernel.
 Grid search and N-fold cross-validation techniques were used
to parameters selection and performance evaluation of the
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14. Experimental Results
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15. Experimental Results cont…
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test result for invmult_kernel

16. Experimental Results cont…
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test result for Laplacian_kernel

17. Experimental Results cont…
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test result for Cauchy_kernel

18. Conclusion and Future Works
 Experimental results indicated that the proposed approach
outperformed the typical SVMs classification algorithm with
different classification accuracies as follow:
 78% for Invmult kernel functions.
 98% for Laplacian kernel function, accuracy is increased by ≈ 20%.
 100% for Cauchy kernel function, accuracy is increased by only 2%.
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19. Conclusion and Future Works
 For future research, variety of challenges and research
directions could be considered. Some general research
directions are to consider more plant diseases with different
conditions
 Another open problem is to tackle the second problem, which
faces SVMs or any classification system; namely feature
selection, using PSO. Moreover, a hybrid approach for
optimizing SVMs parameters and select best features subset is
planned to be developed.
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20. References
 Peralta, E. Iris , and M. David Spooner. “History, origin and early
cultivation of tomato (Solanaceae).” Genetic improvement of
Solanaceous crops, vol. 2, pp. 1-27, 2006.
 Kong, Wai Kin, David Zhang, and Wenxin Li. “Palmprint feature
extraction using 2-D Gabor filters.” Pattern recognition, vol. 36, no. 10,
pp. 2339-2347, 2003.
 Chen, Hui-Ling, Bo Yang, Gang Wang, Su-Jing Wang, Jie Liu, and
DaYou Liu. “Support vector machine based diagnostic system for
breast cancer using swarm intelligence.” Journal of medical systems,
vol. 36, no. 4, pp. 2505-2519, 2012.
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21. Thanks and Acknowledgement
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