Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 148 phenotypic indicators and it is difficult to deeply analyze the interaction mechanism. High-throughput phenotypes can provide rich trait data from various locations, offering materials for analyzing interactions. For instance, we can compare the differences in the sequential growth curves of the same set of materials in experiments in southern Xinjiang and central and southern Hebei, identify the types that still have high yields in southern Xinjiang but reduced yields in the north, and then analyze the reasons in combination with environmental data (temperature, light, etc.). If a certain variety still maintains a high LAI and photosynthetic rate in high-temperature areas, while the leaf area of another variety rapidly decreases under high temperatures, it reveals the difference in heat tolerance between the two. This kind of analysis can be achieved by combining statistical models (such as AMMI models, GGE double-map) with multi-point phenotypic data, or by introducing machine learning to predict variety performance based on environmental variables, and then infer the source of interaction effects. For instance, Xu et al. (2017) utilized a dual-standard analysis of genotype and trait to optimize the registration criteria for cotton varieties. In essence, this was an analysis of the interaction between traits and the environment, aiming to identify trait indicators that can both reflect genetic differences and are robust. High-throughput phenotypic data will make such analyses more accurate. Figure 1 Flow chart of this study (Adopted from Ye et al., 2023) Image caption: (a) Ground control points (GCPs) marked by red circles were evenly arranged in the trial fields. Each accession had about 10-20 plants and was grown in a plot of 3 m × 0.6 m in size. (b) The unmanned aerial vehicle (UAV) remote-sensing platform (DJI Phantom4 RTK) was applied in this study to obtain visible images. (c) Ground-truth plant height (PH) of samples and coordinates of GCPs were measured by ruler and RTK, respectively. (d) Image processing and PH extraction process. The difference between the first and the 95th percentiles was used to extract PH from the on-season digital surface model (DSM) of the plot. (e) UAV-based PH was used for GWAS and the associated candidate genes were identified (Adopted from Ye et al., 2023)
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