Enhancing Mango Leaf Disease Diagnosis Using Convolutional Neural Networks

Abstract

Mango leaf diseases significantly impact crop yield and quality, necessitating early and accurate detection for effective management. This study explores deep learning-based classification using MobileNetV3Small and EfficientNetB0 to automate mango leaf disease identification. A dataset comprising eight classes of healthy and diseased mango leaves was used to train and evaluate the models. The results show that EfficientNetB0 achieved an average accuracy of 99.33% with a loss of 0.0437, outperforming MobileNetV3Small, which attained an accuracy of 99.22% with a loss of 0.0583. The confusion matrix analysis reveals minimal misclassifications, with EfficientNetB0 demonstrating superior precision in distinguishing visually similar diseases. These findings highlight the effectiveness of deep learning models in plant disease classification, with EfficientNetB0 providing a more reliable solution. The study underscores the potential of AI-driven tools for real-time disease detection, which can significantly enhance precision agriculture and sustainable crop management.