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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Agenda

Introduction Problem Definition Motivation Proposed Approach Experimental Results Conclusion and Future Works

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The 11th International Computer Engineering Conference (2015)

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.

Introduction3

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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 4

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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, Egypt

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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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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General proposed approach

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Image preprocessing phase9

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Feature extraction phase10

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Classification phase11

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Overall structure layout12

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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 presented approach.

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Experimental Results14

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Experimental Results cont…15

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test result for invmult_kernel

Experimental Results cont…16

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test result for Laplacian_kernel

Experimental Results cont…17

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test result for Cauchy_kernel

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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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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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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Thanks and Acknowledgement21

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