Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 140 timely manner; (2) Subjective bias: Inconsistent standards among different investigators, resulting in poor data repeatability; (3) Limited spatiotemporal resolution: It is impossible to continuously monitor the growth dynamics of plants, and only data at limited time points can be obtained. (4) Single indicators: It is difficult to determine complex phenotypes such as canopy temperature and photosynthetic parameters in a timely manner by manual methods. With the development of information technology and artificial intelligence, emerging AI-driven phenotypic platforms are gradually overcoming these bottlenecks. The advancement of computer vision and sensor technology enables us to utilize equipment such as drones and robots to obtain massive amounts of crop growth images and environmental data in real time through cameras and various sensors. The rise of AI algorithms such as deep learning enables computers to automatically extract plant features from complex image data, achieving precise recognition and quantification of traits (Ampatzidis and Partel, 2020). Studies have shown that deep learning models such as convolutional neural networks perform exceptionally well in tasks like object detection and image segmentation, and have been successfully applied in phenotypic analyses such as plant organ recognition and pest and disease detection. In recent years, a number of intelligent platforms for field phenotyping have been developed successively at home and abroad, such as unmanned aerial vehicles equipped with multispectral cameras, high-throughput phenotyping tractor systems, and field automatic walking phenotyping robots, etc. (Ye et al., 2023). These AI-driven platforms can efficiently and objectively obtain a large amount of trait data of crops at different growth stages in the field, thereby significantly increasing the data output of field trials. This study will systematically review the current application status and development trends of AI-driven phenotypic analysis in large-scale cotton field experiments, explore its role and prospects in cotton genetic breeding and precision agriculture, analyze the technical basis of AI-driven phenotypic analysis, and summarize the development of high-throughput phenotypic acquisition platforms. Compare the characteristics and advantages of air-based platforms (unmanned aerial vehicles, satellite remote sensing) and ground-based platforms (tractor modification systems, field robots), introduce the integrated application schemes of multiple sensors such as multispectral, hyperspectral, and thermal imaging in cotton phenotypic monitoring, and focus on the processing and analysis of phenotypic data. This paper discusses the extraction of cotton plant structure parameters by image segmentation and 3D reconstruction techniques, the monitoring of plant growth dynamics through time series data analysis, and how to conduct correlation analysis between phenotypic data and genotypes and environmental factors to analyze the genetic mechanism of traits. This study also analyzes the application practice of the AI phenotypic platform through actual cases: including large-scale phenotypic monitoring carried out in China's cotton-growing areas, the application of AI in field experiments and yield prediction in the United States' cotton-growing areas, as well as the performance and challenges encountered by the AI phenotypic platform in multi-site joint experiments. Through the above review and analysis, this study hopes to provide a reference for related research and promote AI phenotypic technology to better serve the genetic improvement and production management of cotton. 2 Cotton Phenotypic Traits and Field Trial Requirements 2.1 Major agronomic traits of cotton and their relationship to yield and fiber quality The agronomic traits of cotton plants are rich and diverse, among which the most important ones include the length of the growth period, plant height, number of fruit branches, number of bolls, single boll weight, coat fraction (ratio of lint cotton to seed cotton), and fiber quality indicators (length, specific strength, Micronization value, etc.). These traits jointly determine the yield and quality performance of cotton. For instance, plant height and branch type affect population structure and light interception efficiency; The number of fruit branches and the weight of individual bolls directly determine the total number of bolls per unit area and the yield of individual bolls, and they are the key factors in the composition of yield. The fabric fraction reflects the proportion of the produced fibers. The length, strength and fineness (Macron value) of the fibers determine the textile quality of cotton fibers (Li, 2024). In breeding practice, it is often necessary to balance yield and quality. Sometimes, there is a negative correlation trend between the two-high-yield varieties may have slightly inferior fiber quality, while high-quality fiber
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