Classification of Diseases in Tomato Leaves with Deep Transfer Learning

Plant diseases are crucial factors because they significantly affect the quality, quantity, and yield of agricultural products. Therefore, early detection and diagnosis of these diseases is important. The overall goal of this study is to develop an acceptable deep-learning model to correctly classify diseases of tomato leaves in RGB color images. To address this challenge, we use a novel approach based on combining two deep learning models VGG16 and ResNet152v2 with transfer learning. The image dataset contains 5500 images of tomato leaves in 5 different classes, 4 diseases (Tomato_Bacterial_spot, Tomato_Early_blight, Tomato_Late_blight, Tomato_Leaf_Mold) and one healthy class (Tomato_healthy). In our experiment, the results are promising and encouraging, showing that the proposed model achieves 99.08% accuracy in training, 97.66% in validation, and 99.02% in testing.