Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN

• URL: http://arxiv.org/abs/2512.17864v2
• Date: Wed, 24 Dec 2025 15:38:23 GMT
• Title: Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN
• Authors: Balram Singh, Ram Prakash Sharma, Somnath Dey,
• Abstract summary: Plant diseases pose a significant threat to global food security.<n>This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection.
• Score: 6.378633888063113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.

Related papers

• Automated Multi-Class Crop Pathology Classification via Convolutional Neural Networks: A Deep Learning Approach for Real-Time Precision Agriculture [0.0]
  This research introduces a Convolutional Neural Network (CNN)-based image classification system designed to automate the detection and classification of eight common crop diseases.<n>The solution is deployed on an open-source, mobile-compatible platform, enabling real-time image-based diagnostics for farmers in remote areas.
  arXiv  Detail & Related papers  (2025-07-12T18:45:50Z)
• Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology [41.34847597178388]
  Vision foundation models (FMs) learn to represent histological features in highly heterogeneous tiles extracted from whole-slide images.<n>We investigate the potential of unsupervised automatic data curation at the tile-level, taking into account 350 million tiles.
  arXiv  Detail & Related papers  (2025-03-24T14:23:48Z)
• Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification [0.0]
  We apply the Automated Concept-based Explanation (ACE) method to plant disease classification.<n>ACE automatically identifies the visual concepts found in the image data and provides insights about the critical features influencing the model predictions.<n>Our findings demonstrate the potential of ACE to improve the explainability of plant disease classification based on deep learning.
  arXiv  Detail & Related papers  (2024-12-10T10:59:43Z)
• Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
  Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape. RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
  arXiv  Detail & Related papers  (2024-07-17T07:44:18Z)
• Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD [3.285994579445155]
  This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases. We propose a sophisticated approach within the domain of subspace learning, known as Higher-Order Whitened Singular Value Decomposition. The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets.
  arXiv  Detail & Related papers  (2024-05-30T13:46:56Z)
• Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment [42.09313885494969]
  We harness the Swin Transformer's capacity to discern extended spatial dependencies within images through the hierarchical framework. Our novel contribution lies in refining local feature representations, orienting them specifically toward the final distribution of the classifier. Our model demonstrates significant robustness and precision, as evidenced by extensive validation of two established benchmarks for Knee OsteoArthritis (KOA) grade classification.
  arXiv  Detail & Related papers  (2024-03-15T01:09:58Z)
• Crop Disease Classification using Support Vector Machines with Green Chromatic Coordinate (GCC) and Attention based feature extraction for IoT based Smart Agricultural Applications [0.0]
  Plant diseases can negatively affect leaves during agricultural cultivation, resulting in significant losses in crop output and economic value. Various machine learning (ML) as well as deep learning (DL) algorithms have been created & studied for the identification of plant disease detection. This article presents a novel classification method that builds on prior work by utilising attention-based feature extraction, RGB channel-based chromatic analysis, Support Vector Machines (SVM) for improved performance.
  arXiv  Detail & Related papers  (2023-11-01T10:44:49Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT [11.623005206620498]
  Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. In this study, a Vision Transformer enabled Convolutional Neural Network model called "PlantXViT" is proposed for plant disease identification. The proposed model has a lightweight structure with only 0.8 million trainable parameters, which makes it suitable for IoT-based smart agriculture services.
  arXiv  Detail & Related papers  (2022-07-16T12:05:06Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.