Detection of Diseases on Tomato Leaves Based on Sub-Classifiers Fuzzy Combination
F. Jakjoud1, A. Hatim2, A. Bouaddi3
1Fatimazahra Jakjoud, Department of Laboratory of Energy Engineering, Materials and Systems, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco.
2Anas Hatim, National Department of Applied Sciences, Cadi Ayad University, Marrakech, Morocco.
3Abella Bouaddi, Department of Laboratory of Energy Engineering, Materials and Systems, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 735-739 | Volume-8 Issue-5, March 2019 | Retrieval Number: E3350038519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Improving agricultural productivity is the subject of several research field, and in order to prevent huge losses automatic identification of plant diseases seems a good solution. In this paper we present an approach based on the K-nearest neighbours (KNN) algorithm and Support vector machine (SVM) to classify tomato leaves images into two classes (normal and sick). We developed two schemes based on a combination of sub-classifications based on KNN and SVM combined with a fuzzy decision maker. The features extractor used in this work is the Haralick approach based on the co-occurrence Matrix. The result of each classifier can reach over than 80% and the best accuracy is given by the Fuzzy Combination of KNN Sub-classifier more than 98%.
Keyword: Plant Disease, Image Classification, Machine Learning, SVM, KNN.
Scope of the Article: Machine Learning