Image detection CNN model for accurate tomato plant disease detection using Inceptionv3 and image augmentation techniques

This research aims to develop a Deep learning-based Convolutional Neural Network (CNN) model for detecting tomato plant diseases. Transfer learning involves training the model on a modest dataset by utilizing characteristics from extensive collections of high-resolution images, all while preserving its capacity to generalize effectively. The model builds upon the Inceptionv3 model and incorporates two dense neural network layers to accurately distinguish between the target object and other objects. The accuracy achieved in classifying the presence of tomato plant diseases is 94.06%.