Real-Time Image-Based Recognition of Mango Leaf Diseases Using Convolutional Neural Networks

Mango is a vital tropical fruit crop, yet its productivity is often reduced by leaf diseases such as Powdery Mildew, Dieback, Anthracnose, Bacterial Canker, and Sooty Mold. These infections lower yield, degrade fruit quality, and cause major economic losses. Early detection is crucial but challenging for farmers with limited expert access.

This study proposes an image-based classification system using Convolutional Neural Networks (CNN) for accurate disease recognition. A curated dataset of mango leaf images was pre-processed and augmented to address class imbalance. The CNN model outperformed traditional classifiers like Support Vector Machine (SVM) and Decision Tree in terms of accuracy, robustness, and efficiency.

The system not only detects multiple diseases with high precision but also offers severity estimation, visual feedback, and farmer-friendly treatment recommendations. Designed for real-time use via smartphones or field cameras, it provides a scalable and accessible solution to support precision agriculture.

Mango leaf disease, CNN, Image classification, Deep learning, Precision agriculture, Real-time detection

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