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Machine Learning Techniques for Tomato Plant Diseases Clustering, Prediction and Classification

The agriculture sector in Palestine faces several challenges that affect the quality of crop yields, including plant diseases. Plant diseases may be caused by bacteria, viruses, and fungus, among others. Early detection and classification of these diseases allow farmers to reduce the instances and control the effect that the disease may have on their crops. Therefore, this study utilizes machine learning models for the clustering, prediction, and classification of tomato plant diseases in Palestine. The study used 3000 smartphone digital images of five tomato plant diseases (Alternaria Solani; Botrytis Cinerea; Panonychus Citri; Phytophthora Infestans; Tuta Absoluta) collected from three districts across the West Bank (Tulkarem, Jenin, and Tubas). The machine learning models used image embedding and hierarchical clustering techniques in clustering, and the neural network, random Forest, naïve Bayes, SVM, Decision Tree, and Logistic regression for prediction and classification. The models’ accuracy was validated in reference to a tomato plant diseases database created by plant pathogens experts. The clustering model provided 7 diseases clustering with an accuracy rate of 70%, while the neural network and logistic regression models reported performance accuracies of 70.3% and 68.9% respectively. The findings demonstrate that the proposed approach provides acceptable accuracy rates in the clustering, detection, and classification of tomato plant disease. Thus, the deployment of machine learning techniques in the agriculture sector might help Palestinian farmers better manage and control tomato diseases.