Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean

Abstract

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single‐time‐point data. Here, we used time‐series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time‐series data, as it may represent an important trait for weed control. Genome‐wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time‐series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single‐time‐point studies. Moreover, this time‐series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time‐series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

UAV‐based time‐series phenotyping reveals the genetic basis of plant height in upland cotton

“…It is a supplement for previous time‐consuming, expensive and inaccurate manual measurement techniques (Wu, Guo, et al., 2019; Xiong et al., 2017; Yang, Feng, et al., 2020; Yang, Yang, et al., 2020). UAV can carry visible cameras and multispectral or hyperspectral cameras, so it can identify not only morphological phenotypes (Volpato et al., 2021; Zhao et al., 2021), such as geometric features (PH, leaf area index, lodging and crop canopy cover) and canopy spectral features (spectral indices), but also physiological phenotypes (Jay et al., 2020; Li, Bai, et al., 2022; Li, Xie, et al., 2022), such as physiological features (chlorophyll, biomass, pigment content and photosynthesis), abiotic and biotic stress indicators (stomatal conductance, canopy temperature difference, leaf water potential and senescence index), nutrients (nitrogen concentration and protein content) and yield (Yang et al., 2017). In addition, during the vigorous growth period of plants, indicators such as PH and leaf area change very significantly.…”

“…Abiotic stress reduces the PH of crops. UAV‐based phenotyping can facilitate the analysis of PH under drought, waterlogging and other adverse climatic conditions (Li, Bai, et al., 2022; Li, Xie, et al., 2022; Xu et al., 2018). In addition to the changes in plant structure caused by abiotic stress during cotton growth, some manmade measures to improve plant type, such as spraying with MC, topping, etc., are often used to manipulate cotton plant architecture to facilitate the mechanized harvesting of cotton fibers (Wu, Du, et al., 2019).…”

“…Li, Bai, et al. (2022) and Li, Xie, et al. (2022) performed factor analysis and principal component analysis (PCA) combined with GWAS to identify dynamic genetic loci for traits.…”

Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles

“…A total of 1,615 cultivars and 190 plots of controls (30 cultivars) were sown on July 15, 2020, and the UAV photos were collected at 17 time points ( Li et al., 2022 ), which represented different growing stages. Some of those cultivars that had either a low germination or were under abiotic/biotic stresses were also studied to maintain high diversity, instead of dropping in the previous study ( Li et al., 2022 ). The soybean cultivars came from worldwide and the largest number of cultivars were from China (70%), followed by the USA and Europe, and covered a wide range of ecotypes.…”

Auxin regulation on crop: from mechanisms to opportunities in soybean breeding

“…Currently, the theory of GmPIN-dependent asymmetric auxin distribution in leaf petiole base can be a reference to study petiole angle formation and to search for an optimal approach for high-density planting in soybean (Fig 3). With the help of time-series phenotypic data collected by an unmanned aircraft system from 1303 soybean varieties, 35 QTL regions are identi ed to associate with canopy coverage (Li et al 2022a), which offers a promising opportunity for soybean breeding with canopy architecture.…”