ArticleFrontiers in plant science2025
Application of real-time detection transformer based on convolutional block attention module and grouped convolution in maize seedling.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- CEHD: A Unified Framework for Detection and Height Estimation of Fresh Corn Ears in Field Conditions.Plants (Basel, Switzerland) · 2025Article
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4 authors.
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Abstract
Introduction: The intelligent detection and counting of maize seedlings constitute crucial components in future smart maize cultivation and breeding. However, the detection of maize seedlings in field environments faces substantial challenges due to their relatively small target size and the complex environment of the farmland. Methods: This study proposed an improved detection model named CBAM-RTDETR. Based on the original feature extraction backbone network of RT-DETR, the model introduced the CBAM module and grouped convolution. Results: The CBAM-RTDETR model achieved a mean Average Precision at 0.5 IoU threshold (mAP0.5) of 92.9%, a mean Average Recall (AR) of 64.4%, and a Frames Per Second (FPS) of 87f/s on the test dataset, all of which are better than the comparison model. Discussion: The proposed model strengthened the shallow edge detail information of the seedlings and increased the feature diversity, effectively addressed the challenges of real-time and accurate identification of maize seedlings in UAV remote sensing images.
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