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.
Highlights
Crop breeding is the art and science of improving crop traits to produce desired characteristics (Sleper and Poehlman, 2006)
It is noted that the selected group had higher lodging readings than the non-selected group, which was possibly caused by the fewer variations in lodging, as most of the breeding lines in the advanced yield trial (AYT) (Figure 5C) had the lodging readings within the range of 1–2, except for a few breeding lines over 3
This study evaluated the performance of a UAV-based High-throughput phenotyping (HTP) system in the selection of superior soybean breeding lines for a breeding program
Summary
Crop breeding is the art and science of improving crop traits to produce desired characteristics (Sleper and Poehlman, 2006). The goal of crop breeding is to improve genetic gains, which is the increase in the performance of a population achieved in 1 year through artificial selection (Fehr, 1991). From 10,000 years ago, plants with preferable traits have been domesticated and selected by humans for increased production and environmental adaptability (Ahmar et al, 2020). Conventional breeding programs still primarily make selection decisions through phenotypic observations that are time-consuming, labor-intensive, and subjective to the experience of breeders (Araus et al, 2018). Genetic gains can be increased by enhancing selection intensity, shortening the breeding cycle, ensuring suitable genetic variation in the population, and obtaining accurate estimates of the genetic values (Xu et al, 2017; Araus et al, 2018; Moreira et al, 2019). The selection intensity (or population size) is limited by the phenotyping capacity to measure key agronomic traits of breeding materials and selection accuracy by the lack of objective and efficient phenotyping tools
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