High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.

Abstract

Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniques to enhance classification accuracy. Real-time leaf images are collected from various agricultural environments to capture a wide range of conditions. To improve image quality and segmentation precision, the Contextual Image Enhancement Wiener Filter (CIEWF) is employed for effective noise reduction. Data augmentation is performed using a Generative Adversarial Network (GAN), increasing dataset diversity and improving model generalization. A novel Region of Interest-based Multi-Dimensional Attention Network (ROI-MDAN) is developed to identify and segment critical disease-affected areas within the leaves. For robust feature extraction, the MSFNet-CAM model is proposed, leveraging parallel multi-scale features and incorporating Coordinate Attention to enhance feature fusion and improve classification performance. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) is used to interpret the model's decision-making process by highlighting the influential regions contributing to disease classification. Experimental results validate the effectiveness of the proposed approach, setting a new benchmark for AI-assisted plant disease diagnosis.

Code and data availability

The paper evaluates on two public leaf-image benchmarks (Cassava Disease Classification Dataset and Groundnut Leaf Dataset) plus a hand-picked dataset, but no author-deposited dataset, code, model, or supplement with an actionable public URL is present in the supplied blocks. The only URL-like fragment (a truncated Kag

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