TumorSageNet CNN hybrid architecture enables accurate detection of mango leaf pathologies.

Abstract

Amidst rising global food security challenges, early and precise detection of plant diseases has become essential-particularly for high-value crops such as mangoes. This study introduces a novel deep learning-based framework for the classification of mango leaf pathologies using advanced convolutional and hybrid neural architectures. A curated dataset of 800 high-resolution mango leaf images, collected from the Rajshahi region of Bangladesh, was preprocessed using extensive data augmentation and color space transformations to enhance generalization. Multiple models, including K-Nearest Neighbors, AlexNet, VGG16, VGG19, and EfficientNet-B7, were evaluated and compared against two proposed architectures: a custom Convolutional Neural Network (CNN) and a hybrid model integrating EfficientNet-B7, Long Short-Term Memory, and attention mechanisms. The proposed CNN model achieved 100% accuracy, precision, recall, and F1-score, outperforming all baseline models. The hybrid model achieved comparable results, demonstrating the effectiveness of combining spatial and temporal feature extraction for plant disease detection. Additionally, Grad-CAM visualizations provided interpretable diagnostic heatmaps, reinforcing the transparency and reliability of the model's predictions. The proposed framework advances state-of-the-art agricultural diagnostics by offering a scalable, interpretable, and high-performing solution for real-time disease monitoring in mango cultivation. These findings hold strong potential for improving crop surveillance and addressing food scarcity in mango-producing regions.

Code and data availability

The paper's mango leaf image dataset (800 images from Rajshahi, Bangladesh) is paper-specific and used directly for the phenotyping/classification analysis, but it is not publicly deposited; the authors state it is available only on request. No public code, models, or supplementary data assets are provided.

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