ArticlePlants (Basel, Switzerland)2023
A High Performance Wheat Disease Detection Based on Position Information.
Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Regional feature purification contrastive learning for wheat biotic stress detection.Frontiers in plant science · 2025Article
- Multisource information fusion method for vegetable disease detection.BMC plant biology · 2024Article
- Investigating attention mechanisms for plant disease identification in challenging environments.Heliyon · 2024Article
- The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop.Sensors (Basel, Switzerland) · 2023Article
- An Accurate Classification of Rice Diseases Based on ICAI-V4.Plants (Basel, Switzerland) · 2023Article
- Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features.Frontiers in plant science · 2023Article
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Authors and funding
8 authors.
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Abstract
Protecting wheat yield is a top priority in agricultural production, and one of the important measures to preserve yield is the control of wheat diseases. With the maturity of computer vision technology, more possibilities have been provided to achieve plant disease detection. In this study, we propose the position attention block, which can effectively extract the position information from the feature map and construct the attention map to improve the feature extraction ability of the model for the region of interest. For training, we use transfer learning to improve the training speed of the model. In the experiment, ResNet built on positional attention blocks achieves 96.4% accuracy, which is much higher compared to other comparable models. Afterward, we optimized the undesirable detection class and validated its generalization performance on an open-source dataset.
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