ArticleFrontiers in plant science2022
Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- Financial time series forecasting with a hybrid VMD-CSA-BiT framework.Scientific reports · 2026Article
- CKM-YOLO11: A Lightweight Maize Foliar Disease Detection Model for Complex Natural Field Environments.Sensors (Basel, Switzerland) · 2026Article
- PlantIF: Multimodal semantic interactive fusion via graph learning for plant disease diagnosis.Plant phenomics (Washington, D.C.) · 2026Article
- A novel lightweight hybrid CNN-ViT for maize leaf disease classification.Scientific reports · 2026Article
- Quantitative morphological phenotyping of infection structures in cucumber downy mildew and powdery mildew.Frontiers in plant science · 2026Article
- LeafFusionNet: a hybrid deep learning approach for robust plant disease detection.Frontiers in artificial intelligence · 2026Article
- Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition.Scientific reports · 2025Article
- YOLO-TPS: A Multi-Module Synergistic High-Precision Fish-Disease Detection Model for Complex Aquaculture Environments.Animals : an open access journal from MDPI · 2025Article
- AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming.Scientific reports · 2025Article
- Fruit Freshness Classification and Detection Based on the ResNet-101 Network and Non-Local Attention Mechanism.Foods (Basel, Switzerland) · 2025Article
- A Comprehensive Survey of Deep Learning Approaches in Image Processing.Sensors (Basel, Switzerland) · 2025Review
- MoSViT: a lightweight vision transformer framework for efficient disease detection via precision attention mechanism.Frontiers in artificial intelligence · 2025Article
- Maize disease classification using transfer learning and convolutional neural network with weighted loss.Heliyon · 2024Article
- Implementing Real-Time Image Processing for Radish Disease Detection Using Hybrid Attention Mechanisms.Plants (Basel, Switzerland) · 2024Article
- A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds.Plants (Basel, Switzerland) · 2024Article
- Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.Scientific reports · 2024Article
- SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN.Scientific reports · 2024Article
- From laboratory to field: cross-domain few-shot learning for crop disease identification in the field.Frontiers in plant science · 2024Article
- Automatic classification of ligneous leaf diseases via hierarchical vision transformer and transfer learning.Frontiers in plant science · 2023Article
- Transfer learning for versatile plant disease recognition with limited data.Frontiers in plant science · 2022Article
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4 authors.
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Abstract
Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.
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