Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 149 6 Case Studies: Practical Applications of AI-Driven Cotton Phenotyping Platforms 6.1 Large-scale phenotyping practices in Chinese cotton regions China is a major country in cotton cultivation and scientific research. In recent years, China has made many attempts in phenotypic monitoring of large-scale cotton fields. A typical example is the phenotypic identification of a large number of cotton germplasm resources in major production areas such as Xinjiang. The ecological environment in Xinjiang is rather unique, and it is also rich in cotton resources. However, to find germplasm that is both stress-resistant and high-yielding from so many materials, it would be very slow and arduous to use traditional methods. To enhance efficiency, researchers have introduced a high-throughput phenotypic platform to conduct large-scale and precise field determinations on hundreds of cotton germplasms. For instance, Professor Zhang Xianlong's team from Huazhong Agricultural University collaborated with research institutions in Xinjiang to conduct high-throughput phenotypic monitoring using drones. They screened over 1 800 cotton hybrid offspring materials in terms of plant height, leaf traits, and yield. Through high-frequency remote sensing monitoring, they selected 53 cotton materials with particularly strong drought resistance in the fields. Even in the case of severe drought, when the irrigation water volume was reduced by 50%, the output of these materials did not decline. Next, the research team combined molecular marker analysis and applied these excellent materials in breeding, eventually developing new water-saving and drought-resistant varieties like "Jinken 1161". This case demonstrates that high-throughput phenotypic technology has played a significant role in the screening of cotton resources and stress-resistant breeding in China, and has also greatly accelerated the breeding speed. High-throughput phenotypic monitoring has also been carried out at the Nanfan base in Hainan. There are numerous plots for the southern breeding and generation increase experiment, which poses a great challenge to manual management. The Chinese Academy of Agricultural Sciences has established the National Nanfan Crop Phenotyping Center in Sanya, equipped with automated phenotyping carts and unmanned aerial vehicle systems, to conduct all-weather monitoring of crops such as cotton and rice in the experimental fields (Zhang et al., 2024). It is reported that the ground phenotypic vehicle of the center can automatically tour multiple experimental fields every day, monitor the plant height and growth progress of cotton, and can transmit the data to the remote server in real time. Breeders can view the growth curves and on-site images of each material through their mobile phones or computers, keep abreast of the progress of experiments in a timely manner and detect any abnormal situations. This remote digital monitoring model has particularly played a role during the epidemic, enabling breeders who were unable to go to the site to "select seedlings remotely". 6.2 AI-driven field trials and yield prediction applications in U.S. cotton regions In the United States, several cotton-growing states have established high-throughput field phenotypic facilities. Among them, the most well-known is the USDA (USDA-ARS) Crop Field High-throughput Phenotyping Facility located in Maricopa, Arizona. This base is equipped with a large Field Scanalyzer high-throughput automated phenotypic frame (mainly used for wheat, etc.), but for cotton, experiments and monitoring are mainly conducted using a combination of unmanned aerial vehicles and ground platforms. Thorp et al. (2024) reported a study on the assessment of chlorophyll content in cotton leaves using ground hyperspectral and machine learning in the Arizona cotton region. They conducted a four-year field experiment in Maricopa, repeatedly measuring the leaf reflectance of different cotton varieties using a high spectrometer carried on a handcart, and trained the model with the chlorophyll content analyzed in the laboratory as the standard. By comparing 148 spectral indices and 14 machine learning algorithms, they found that ensemble learning (such as random forest and gradient boosting) combined with red-edge band indicators could best predict chlorophyll, with an R²of up to 0.88. However, they also found that the model had difficulties in generalization across different years: when trained with 2019-2020 data and tested in 2021-2022, the prediction performance was poor (R²was only 0.46). This prompt requires calibration for environmental differences (Figure 2). In terms of production prediction and precision agriculture, a large number of cases of AI application have emerged in the cotton-growing areas of the United States. Feng et al. (2022) comprehensively considered soil, meteorological and unmanned aerial vehicle remote sensing information and used deep learning to predict cotton yield. They set up field trials in Missouri to measure yields under different treatments and collect soil texture,
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