Plant disease is one of the most threatening factors in agriculture field causing a decrease in the quality and quantity of produced products. Some of the diseases can be identified and recognized by the appearance of symptoms on the leaves of the plant. Non-destructive and accurate techniques for detection of diseases could be practical in increasing productivity and decreasing the waste of products. In this research, nine types of common diseases in tomatoes were evaluated by the machine vision method and using Gray Level Co-occuarance Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Pattern (LBP) texture features. Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) were used to model the dataset. The best algorithm and model were introduced using SVM and KNN models. With the ANN model, the best results were obtained with the GLCM feature, while in other models, the features extracted from the GLRM algorithm exhibited the best outcomes. The SVM model with the cubic kernel function yielded the best results, which had the accuracy of 97.43% and 91.38% in training and test steps. The sensitivity and specificity of this modeling were 99.46% and 95.59% as well as 97.91% and 86.75% for training and test datasets, respectively. In addition, the results were improved using the Genetic Bee Colony (GBC) feature reduction algorithm. The results exhibited acceptable performance in detecting healthy and unhealthy leaves, and in accurately diagnosing the type of tomato disease.
