A Transparent Deep Learning Approach for Mango Leaf Disease Classification

Mango leaf diseases pose a significant threat to crop yield and quality, directly impacting agricultural productivity and farmer livelihoods. Accurate and timely detection of these diseases is essential for effective disease management and sustainable farming practices. This project proposes a deep learning-based approach for automated mango leaf disease classification using the Xception (Extreme Inception) model, a state-of-the-art convolutional neural network (CNN) architecture. Xception leverages depthwise separable convolutions to efficiently capture fine-grained patterns such as texture, color variations, and lesion shapes in leaf images, enabling high accuracy in disease classification. To ensure data privacy and scalability, the system incorporates federated learning, allowing multiple agricultural institutions or farmer cooperatives to collaboratively train the model without sharing raw data. This decentralized approach prevents unauthorized access to sensitive datasets while continuously improving model performance across diverse environments. The proposed system thus combines advanced deep learning techniques with privacy-preserving collaborative learning, offering a robust, secure, and scalable solution for early detection and classification of mango leaf diseases. Its implementation can contribute significantly to precision agriculture, reducing economic losses and supporting sustainable crop management.