In order to classify diseases in plants, techniques like computer vision and image processing have been used from the last decade. For identification and classification of Tomato diseases, we trained a model on Tomato plant leaves. In the first step, the leaf regions are being identified by performing the image segmentation with the help of the image processing technique. Then, Histogram of Oriented Gradients (HOG) is utilized to separate highlights from the fragmented pictures and train six distinctive AI models (Support Vector Machine (SVM), Naive bayes, Decision tree, K-nearest neighbor (K-NN), Random Forest, Logistic Regression and Decision Tree) on Tomato plant leaves using HOG features. The proposed models are tried on the picture dataset of five unique classis (Tomato bacterial spot, Tomato sound, Tomato mosaic infection, Tomato spotted creepy crawly parasite, and Tomato yellow leaf twist infection). After comparison of the machine learning classifiers, SVM ranked first amongst the classifiers that achieve 92 % classification accuracy on Tomato plant leaves disease image dataset.
