Enhanced Deep Residual Network based Self-Learning framework for Mango leaf disease Classification: Focus on Anthracnose and Grey Blight

Abstract

In modern agriculture, ensuring early and accurate diagnosis of plant diseases is vital for crop health and yield. Deep learning has increasingly been utilized for diagnosing diseases in mango leaves using pathological images. Most existing solutions rely heavily on supervised learning models, which require extensive labeled data—a process that is both time-consuming and labor-intensive for agricultural experts. To ease this burden, a self-supervised learning approach has been developed that depends on minimal labeled data. This proposed work introduces a semi-supervised learning model for classifying mango leaf diseases, reducing the need for exhaustive manual annotation. The system is trained using 3,654 images of diseased mango leaves. BYOL (Bootstrap Your Own Latent), a self-supervised algorithm, was employed to train a ResNet with SE blocks network, enabling it to extract meaningful features from infected regions without relying entirely on labeled data. With only 30% of the dataset labeled, a self-supervised learning approach was used to develop a classification model for identifying Anthracnose, Grey Blight, and healthy leaves. This technique achieved an impressive classification accuracy of 98.11%, slightly surpassing the fully supervised ResNet-50 model's accuracy of 97.62%. The outcome demonstrates that accurate disease detection in mango leaves can be accomplished with reduced labeling effort, supporting more efficient and scalable agricultural diagnostics.