Detection of Tomato Leaf Diseases Using Transfer Learning Architectures: A Comparative Analysis

Plant diseases that can destructively harm the agriculture are usually spotted with bare eyes, and this may consume a longer time leaving a chance of incorrect detection. Early detection can solve this problem and reduces the risk of decreasing plant production. The central focus of this study is to automatically detect tomato plant leaf diseases in a faster way by implementing Deep Learning (DL), which can be effectively applied for image classification using different convolutional neural network (CNN) architectures. This paper offers to classify images via transfer learning architectures including VGG-16, VGG-19, and Inception-V3 models with CNN to identify diseases. Applying these models on a dataset containing 11000 images of tomato leaves including signs of 9 different diseases and healthy leaf, high accuracy is gained. Finally, the comparative analysis done on the three pre-trained models within this paper summarizes that the best-suited model for detecting tomato leaf diseases with the best accuracy is Inception-V3. In order to support the claim, necessary results and data are also included in this paper.