Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 141 varieties often have lower yields. This makes a deep understanding of the relationships among the main agronomic traits particularly important. A large number of genetic statistical studies have revealed the correlation patterns among cotton traits. For instance, field experiment analyses of multiple cotton varieties (lines) have shown that the yield of lint cotton is significantly positively correlated with yield components such as the number of bolls per plant and the weight of each boll, and is often positively correlated with plant height. Taller plants usually produce more bolls and have higher yields. Meanwhile, there is a certain correlation between output and certain fiber quality characteristics. For instance, materials with higher output may have a moderately higher Macron value (an indicator of fiber fineness), but the fiber length may be shorter. Deng et al. (2020) conducted an experimental analysis of 63 new early-maturing upland cotton varieties and found that the seed cotton yield was extremely significantly positively correlated with the growth period, plant height, number of boll formation, and single boll weight, and was also positively correlated with the fiber Macron value and uniformity index. It is indicated that moderately extending the growth period, increasing the plant type, the number of knots and the weight of individual knots can simultaneously increase the yield and improve the fiber quality to a certain extent. On the other hand, environmental conditions have a regulatory effect on the relationship between traits. For instance, under conditions of sufficient water and fertilizer, increasing plant height and the number of branches is beneficial for enhancing yield. However, in drought or high-density planting, overly tall plants may instead lead to lodging and reduced yields. 2.2 Demand for phenotypic data acquisition in large-scale field trials Large-scale field trials of cotton usually involve the planting comparison of numerous varieties (lines) at multiple locations and in multiple seasons to assess the high yield, stable yield and adaptability of the materials. The characteristic of this type of experiment is that it is extremely large in scale: a single experiment may involve hundreds of materials, which are repeatedly planted in different environments. How to obtain detailed and reliable phenotypic data on such a large experimental scale is a major challenge facing researchers. First of all, in experiments involving hundreds or even thousands of cells, relying on traditional manual measurement is obviously not feasible-the human input is huge and difficult to complete in a timely manner, and the data at different locations during the same period is also difficult to ensure consistency (Adams et al., 2020). However, breeders urgently need comprehensive phenotypic information to discover superior materials and identify genes that control traits. Therefore, large-scale trials have put forward an urgent need for efficient acquisition of phenotypic data: (1) There is a need for measurement methods that can cover a large area and multiple materials at one time, and it is best to complete the data collection of the entire trial in a short time to eliminate the influence of environmental diurnal variation; (2) Objective and standardized measurements are needed to enhance the consistency of data from different observation personnel at different locations. (3) Multiple repeated measurements are required during the growth period to obtain information on the dynamic changes of traits. Only by meeting these requirements can the advantages of large-scale experimental design be fully leveraged to screen out types with truly outstanding genetic performance from a vast amount of materials, and ensure the credibility and stability of the screening results. The emergence of high-throughput phenotypic technology precisely meets the above demands. For instance, the unmanned aerial vehicle (UAV) remote sensing platform can obtain the canopy images and growth parameters of the entire experimental field in a single flight, enabling a single person to acquire data from thousands of plots in a single day and significantly enhancing efficiency. Studies show that during the rapid growth stage of cotton, the daily changes in traits such as plant height are significant. It is difficult to accurately capture these changes solely relying on manual labor. However, tools like drones can achieve high-frequency monitoring and obtain continuous growth curves. Ye et al. (2023) pointed out that during the rapid growth period of cotton, the daily growth of plants is very large, and it is "almost unrealistic" to carry out large-scale manual measurement. However, unmanned aerial vehicle remote sensing can accurately obtain the plant height dynamics of different materials throughout the field. 2.3 Value of phenotypic big data in cotton breeding and precision cultivation In terms of breeding, phenotypic big data provides a prerequisite for analyzing the genetic mechanisms of complex quantitative traits. Traditional QTL mapping and association analysis are often limited by the volume and
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