Automated Detection of Tomato Leaf Blight Using Convolutional Neural Networks and Image Processing Techniques

This study presents an automated approach for detecting Tomato Leaf Blight using convolutional neural networks (CNNs) applied to leaf images. The methodology leverages image preprocessing techniques, such as segmentation and feature extraction, to enhance classification accuracy. A dataset of 5,000 tomato leaf images was used to train and validate the CNN model, achieving an accuracy of 92%. The results demonstrate the potential of CNNs in early disease detection, aiding farmers in timely interventions. Future work includes integrating real-time monitoring systems.