Leveraging Artificial Intelligence for Tomato Leaf Disease Diagnosis: A Performance Comparison of DL Models

Early prediction of the tomato leaf disease could benefit farmers in the agriculture field. It can reduce yield loss by detecting the disease promptly. This paper employed three distinct models of Deep Learning for the early prediction of the leaf disease, such models are Convolutional Neural Network, EfficientNetB3, and VGG16. This research article works on the classification of the various kinds of tomato leaf disorders, 10 different leaf disorders are used with a total of 10,000 images, and each disorder has 1000 images. Among the three models, the EfficientNetB3 model performed better with a testing accuracy of 98%, with a minimum testing loss of 0.041 loss. This model performed better because of its depth-wise, and widthwise scaling, and optimized compound scaling of the networks.