Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning.

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 lea

SUBMITTER: Zhou L 

PROVIDER: S-EPMC12710009 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature