ArticlePlant phenomics (Washington, D.C.)2025
Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning.
Article in Plant phenomics (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- A multi-scale supervised contrastive framework for cross-domain soybean disease classification using leaf and UAV imagery.Scientific reports · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment.Frontiers in plant science · 2026Article
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Authors and funding
11 authors.
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Abstract
The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 kg ha
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