Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 147 high-resolution images combined with three-dimensional information, the changes in leaf area can be dynamically tracked. This can be used to judge the defoliation effect or assess the aging condition of the plant. In addition to plant height and LAI, the AI platform can also extract some structural information of cotton that is difficult to measure by hand. 5.2 Time-series data analysis and growth dynamics monitoring A direct application of time series data analysis is the quantification of growth rates. Traditionally, we have roughly understood the early or late maturity of a variety by measuring the traits at several stages such as the seedling stage, bud formation stage, flowering stage and catkins stage. Nowadays, it is possible to monitor on a daily basis or even at a higher frequency by using drones and ground sensors. For instance, Ye et al. (2023) utilized drones to measure the plant height of 320 upland cotton materials at three different locations in a time series, obtaining the plant height values of each material at multiple time points. By conducting principal component analysis (PCA) on these time series data, they reduced the dimension of the plant height growth curve and extracted two main components: the first component represents the average plant height level, and the second component reflects the difference in growth rate. The results showed that the materials were classified into different types. Some were tall and grew fast as a whole, some were short and grew slowly, and some were of medium height but grew rapidly in the early stage and slowed down in the later stage (Figure 1). This analysis reveals information that traditional endpoint measurements cannot provide-the growth dynamic patterns of different materials. For breeding, this helps to screen out variety combinations that grow fast during the seedling stage and stably after flowering, and can also discover some materials with late-blooming advantages (growing fast in the later stage and possibly achieving high yields in specific environments). Through time series monitoring, we can identify which stages in the growth of cotton are the most critical and also see how much impact these stages have on the yield. For instance, if the analysis of NDVI or LAI time series data from multiple locations over several consecutive years reveals that a certain stage (such as the initial flowering period) has the strongest relationship with yield, it indicates that this stage is of great significance. In breeding and field management, special attention should be paid to the growth conditions during this period. In actual production, it is often said that the "peach setting period" (that is, the flowering and bell-bearing period) is particularly important. This is because this stage determines how many bolls the cotton can produce and how big each boll can grow. Now, we can prove this matter with data. If the time series data shows that within two weeks after flowering, the LAI of some plots rises rapidly and the final yield is also high, it indicates that the increase in leaf area at this stage is very helpful for the yield. In this way, we can, based on this rule, increase the input of water and fertilizer during this period to boost the output. This is like installing a "warning system" during the growing season. It can tell us whether the cotton is growing well. If it doesn't meet the standards, we can also adjust the management methods in time. 5.3 Association analysis of phenotype, genotype, and environmental factors In recent years, with the development of high-throughput genotyping technology, we can obtain whole-genome marker information (such as SNPS) of cotton materials, while high-throughput phenotypic platforms provide a vast amount of trait data. The combination of the two has given rise to a new paradigm of the integration of "phenomics" and "genomics". In terms of genotype-phenotypic association analysis, genome-wide association analysis (GWAS) and quantitative trait loci (QTL) mapping are the main approaches. High-quality phenotypic data is directly related to the success or failure of association analysis. In the past, due to the large errors and few repetitions in artificial phenotypic data, only a few major genes could often be detected. Nowadays, AI phenotypic platforms provide more refined and multi-dimensional phenotypic indicators for association analysis, thereby enhancing the detection efficiency. On the other hand, phenotypic-environmental interaction analysis is crucial for understanding variety adaptability and optimizing agricultural management. The phenotypic manifestations of cotton varieties vary in different environments, that is, there exists a "G×E interaction". Traditional breeding evaluates the wide adaptability and specific adaptability of varieties through multi-point experiments, but it is limited by the single artificial
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