ArticleHeliyon2024
Investigating attention mechanisms for plant disease identification in challenging environments.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- An attention-augmented lightweight convolutional framework for fine-grained plant leaf disease classification.Frontiers in plant science · 2026Article
- From architectural complexity to minimalist optimization: refining microscopic tea disease detection via Opti-YOLOv11n.Frontiers in plant science · 2026Article
- MFK-Net: a computationally efficient Mamba-Fourier-KAN hybrid architecture for UAV-based crop classification.Frontiers in plant science · 2026Article
- Automated detection of defective coffee beans based on improved YOLOv10 framework.Current research in food science · 2026Article
- Deep learning-based disease detection in potato and mango leaves: a comparative study of CNN, AlexNet, ResNet, and EfficientNet.Scientific reports · 2025Article
- Edge-enhanced dual branch CNN with adaptive attention for robust apple leaf disease detection.BMC plant biology · 2025Article
- Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.Scientific reports · 2025Article
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Spatial attention-guided pre-trained networks for accurate identification of crop diseases.Scientific reports · 2025Article
- Enhanced multiscale plant disease detection with the PYOLO model innovations.Scientific reports · 2025Article
- Leaf disease detection and classification in food crops with efficient feature dimensionality reduction.PloS one · 2025Article
- An ensemble heterogeneous transformer model for an effective diagnosis of multiple plant diseases.Frontiers in plant science · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.
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