Identifying Key Traits for Screening High-Yield Soybean Varieties by Combining UAV-Based and Field Phenotyping
The breeding of high-yield varieties is a core objective of soybean breeding programs, and phenotypic trait-based selection offers an effective pathway to achieve this goal. The aim of this study was to identify the key phenotypic traits of high-yield soybean varieties and to utilize these traits for screening high-yield soybean varieties. In this study, the UAV (unmanned aerial vehicle)- and field-based phenotypic data were collected from 1923 and 1015 soybean breeding plots at the Xuzhou experimental site in 2022 and 2023, respectively. First, the soybean varieties were grouped by using a self-organizing map and K-means clustering to investigate the relationships between various traits and soybean yield and to identify the key ones for selecting high-yield soybean varieties. It was shown that the duration of canopy coverage remaining above 90% (Tcc90) was a critical phenotypic trait for selecting high-yield varieties. Moreover, high-yield soybean varieties typically exhibited several key phenotypic traits such as rapid development of canopy coverage (Tcc90r, the time when canopy coverage first reached 90%), prolonged duration of high canopy coverage (Tcc90), a delayed decline in canopy coverage (Tcc90d, the time when canopy coverage began to decline below 90%), and moderate-to-high plant height (PH) and hundred-grain weight (HGW). Based on these findings, a method for screening high-yield soybean varieties was proposed, through which 87% and 72% of high-yield varieties (top 5%) in 2022 and 2023, respectively, were successfully selected. Additionally, about 9% (in 2022) and 10% (in 2023) of the low-yielding (bottom 60%) were misclassified as high-yielding. This study demonstrates the benefit of high-throughput phenotyping for soybean yield-related traits and variety screening and provides helpful insights into identifying high-yield soybean varieties in breeding programs.
Authors
- Guijun Yang (ORCID: https://orcid.org/0000-0002-6425-8321)
- Haorang Wang
- Jiaoping Zhang
- Yang Chen (ORCID: https://orcid.org/0000-0002-3575-3163)
- Simeng Li (ORCID: https://orcid.org/0009-0008-8679-5532)
- Haikuan Feng (ORCID: https://orcid.org/0000-0003-3312-6200)
- Heli Li (ORCID: https://orcid.org/0000-0003-3028-6479)
- Di Pan (ORCID: https://orcid.org/0000-0002-2320-7835)
- Pengting Ren
Institutions
- Nanjing Agricultural University (CN)
- Chang'an University (CN)
- Sweet Potato Research Institute (CN)
- Ministry of Agriculture and Rural Affairs (CN)
- National Engineering Research Center for Information Technology in Agriculture (CN)
- Henan Polytechnic University (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-02-18
- DOI
- https://doi.org/10.3390/rs17040690
- Citations
- 5
- Primary Topic
- Soybean genetics and cultivation
- Type
- article
- Field-Weighted Citation Impact
- 8.75
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China