UAV-BASED HIGH-THROUGHPUT PHENOTYPING OF SOYBEAN USING LIGHTWEIGHT POINT DETECTION FOR MULTI-ORGAN TRAIT EXTRACTION

Abstract

Accurate soybean field phenotyping is increasingly important for breeding. However, traditional measurement methods are labor-intensive and subjective, while UAV-based approaches are challenged by complex backgrounds and densely distributed small targets. This study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry. A lightweight point-based model, Soy-MOPNet, is then proposed for fast and parallel detection of soybean seeds and stem nodes. The model incorporates the proposed SDConv, optimized hierarchical dilated convolution (HDC) principles, and PBOS to enhance adaptive feature fusion, receptive field design, and multi-branch training stability, respectively. Based on the detected keypoints, six phenotypic traits are extracted in parallel, providing comprehensive support for field phenotyping, breeding selection, and precision agricultural management.

Code and data availability

The supplied blocks describe a UAV soybean phenotyping dataset (424 images, 26 cultivars) and the Soy-MOPNet model, but contain no data or code availability statement, no public repository, and no author-provided URL for the dataset, images, annotations, code, or trained model. All URLs in the text are cited references

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