Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

Jing Zhou; Eduardo Beche; Caio Canella Vieira; Dennis Yungbluth; Jianfeng Zhou; Andrew Scaboo; Pengyin Chen

doi:10.3389/fpls.2021.768742

Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

Front Plant Sci. 2022 Jan 11:12:768742. doi: 10.3389/fpls.2021.768742. eCollection 2021.

Authors

Jing Zhou¹, Eduardo Beche¹, Caio Canella Vieira^{1

2}, Dennis Yungbluth¹, Jianfeng Zhou¹, Andrew Scaboo¹, Pengyin Chen^{1

2}

Affiliations

¹ Division of Plant Science and Technology, University of Missouri, Columbia, MO, United States.
² Fisher Delta Research Center, University of Missouri, Portageville, MO, United States.

Abstract

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

Keywords: Unmanned Aerial Vehicle (UAV); high-throughput phenotyping; machine learning; soybean breeding; variety selection.