Tomato Plant Disease Classification Using Transfer Learning

Detecting and categorizing diseases in tomato plants poses a significant hurdle for farmers, resulting in considerable agricultural losses and economic harm. The prompt underscores the significance of promptly identifying and classifying diseases to enact successful management strategies. Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in tasks involving image classification, notably in categorizing diseases that impact tomato plants. However, CNN models can be computationally expensive to train and require large datasets of labeled images. Utilizing advanced CNN models can enhance the efficacy of classification models for tomato plant diseases, simultaneously decreasing computational expenses and the demand for extensive training data. Enhanced CNN models can be developed using a variety of techniques, such as transfer learning, data augmentation, and residual networks. This project aims to implement a tomato plant disease classification model using an enhanced convolution neural network. This work uses the lifelong learning method which is the model that allows one to learn new tasks without forgetting previous knowledge. Leveraging sophisticated CNN models can improve the effectiveness of classification models for tomato plant diseases, while also reducing computational costs and the need for extensive training data. It is beneficial for tasks where there is limited data available to train a model from scratch.