ArticlePlant methods2024
Crop insect pest detection based on dilated multi-scale attention U-Net.
Article in Plant methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Explainable VGG16 transfer learning with SHAP and grad-CAM for wax moth pest and infestation detection in honeybee apiaries using imaging data.Scientific reports · 2026Article
- AWAVM-UNet: adaptive-weighted attention VM-UNet with multiscale attention feature aggregation for grape disease detection from UAV imagery.Frontiers in plant science · 2026Article
- PestRefineDet: integrating DenseNet with RefineDet for precision pest detection in agriculture.Frontiers in plant science · 2026Article
- AGMSF-VMUNet: attention-guided multi-scale feature fusion VM-UNet for crop pest detection.Frontiers in plant science · 2026Article
- CFAEVM-UNet: cross-fusion attention enhanced VM-UNet for UAV image segmentation of small and dense field crop pests.Frontiers in plant science · 2026Article
- Robust agricultural pest detection under occlusion and environmental variations via AGIIN-MAF training and SAODL adaptation.Frontiers in plant science · 2026Article
- Attention-PestNet: hierarchical scaled dot-product attention for insect pest detection.Plant methods · 2025Article
- Visual feature-based multi-scale hybrid attention network for fine-grained Hawthorn varieties identification.Scientific reports · 2025Article
- GCASSN: a graph convolutional attention synergistic segmentation network for 3D plant point cloud segmentation.Frontiers in plant science · 2025Article
- Article
- Paddy pest image segmentation based on multiscale attention fusion VM-UNet.Frontiers in plant science · 2025Article
- MAVM-UNet: multiscale aggregated vision MambaU-Net for field rice pest detection.Frontiers in plant science · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundCrop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests.
methodsAiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image.
resultsThe experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively.
conclusionThe results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system.
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