Mango Leaf Disease Classification Using Deep Learning Techniques

Mango leaf diseases are important threats to the world's mango production, for which effective and timely detection methods are required. In this work, we have investigated the methodology of Convolutional Neural Network, namely the VGG19 architecture, in detecting various mango leaf diseases and make recommendations on how to protect mango crops. We used a dataset of healthy and diseased mango leaf images, which had been pre-processed to support model accuracy. The VGG19 model, due to its depth and feature extraction abilities, was trained and compared with conventional image processing methods. Our findings indicate that the CNN based on the VGG19 had a high classification accuracy substantially better than traditional approaches. This paper emphasizes the use of deep learning methods in the management of diseases in agriculture, opening doors for real- time intervention and monitoring methods to protect mango crops. The future task will be to expand the dataset and utilize transfer learning methods to strengthen the model improve model robustness and usability in various agricultural scenarios.