ArticleFrontiers in plant science2022
A hybrid attention-enhanced DenseNet neural network model based on improved U-Net for rice leaf disease identification.
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 7 papers.
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7 citing papers in PubMed.
- OpenPlant: A Large-Scale Benchmark Dataset for Agricultural Plant Classification Using CNNs, ViTs, and VLMs.Plants (Basel, Switzerland) · 2026Article
- Deep learning techniques for early detection and classification of leaf diseases in crops.Frontiers in plant science · 2026Article
- LDL-MobileNetV3S: an enhanced lightweight MobileNetV3-small model for potato leaf disease diagnosis through multi-module fusion.Frontiers in plant science · 2025Article
- EMSAM: enhanced multi-scale segment anything model for leaf disease segmentation.Frontiers in plant science · 2025Article
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- A ResNet50-DPA model for tomato leaf disease identification.Frontiers in plant science · 2023Article
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Abstract
Rice is a necessity for billions of people in the world, and rice disease control has been a major focus of research in the agricultural field. In this study, a new attention-enhanced DenseNet neural network model is proposed, which includes a lesion feature extractor by region of interest (ROI) extraction algorithm and a DenseNet classification model for accurate recognition of lesion feature extraction maps. It was found that the ROI extraction algorithm can highlight the lesion area of rice leaves, which makes the neural network classification model pay more attention to the lesion area. Compared with a single rice disease classification model, the classification model combined with the ROI extraction algorithm can improve the recognition accuracy of rice leaf disease identification, and the proposed model can achieve an accuracy of 96% for rice leaf disease identification.
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