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

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Bibliographic Details
Title: High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.
Authors: Sudhakar, R., Nithya, K., Dhivyaa, C. R., Sharmila, C.
Source: Scientific Reports; 11/26/2025, Vol. 15 Issue 1, p1-26, 26p
Subject Terms: LEAF diseases & pests, DEEP learning, ARTIFICIAL neural networks, IMAGE enhancement (Imaging systems), FEATURE extraction, DATA augmentation, AGRICULTURE, ARTIFICIAL intelligence
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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
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. [ABSTRACT FROM AUTHOR]
ISSN:20452322
DOI:10.1038/s41598-025-26144-4