Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

The South Indian mango industry is confronting severe threats due to various leaf diseases, which significantly impact the yield and quality of the crop. The management and prevention of these diseases depend mainly on their early identification and accurate classification. The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks (CNNs) as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees. Our study collected a rich dataset of leaf images representing different disease classes, including Anthracnose, Powdery Mildew, and Leaf Blight. To maintain image quality and consistency, pre-processing techniques were employed. We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification. This proposed CNN model was trained and evaluated using our collected dataset. The customized deep CNN model demonstrated high performance in experiments, achieving an impressive 93.34% classification accuracy. This result outperformed traditional CNN algorithms, indicating the potential of customized deep CNN as a dependable tool for disease diagnosis. Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models. Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees. These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices, thereby mitigating the issues currently facing the South Indian mango industry.