Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 139 Feature Review Open Access AI-Driven Phenotyping Platforms for Large-Scale Cotton Field Trials Xiaojing Yang, Xiaoyan Chen, Yuxin Zhu Modern Agriculture Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding email: yuxinzhu@cuixi.org Field Crop, 2025, Vol.8, No.3 doi: 10.5376/fc.2025.08.0014 Received: 02 Apr., 2025 Accepted: 13 May, 2025 Published: 04 Jun., 2025 Copyright © 2025 Yang et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Yang X.J., Chen X.Y., and Zhu Y.X., 2025, AI-driven phenotyping platforms for large-scale cotton field trials, Field Crop, 8(3): 139-153 (doi: 10.5376/fc.2025.08.0014) Abstract Cotton, as an important economic crop, its yield and quality are directly related to the development of the textile industry and agricultural economy. This study summarizes the relationship between the main agronomic traits of cotton (such as plant height, number of bolls, fiber quality, etc.) and yield and quality, discusses the urgent need for obtaining phenotypic data in large-scale field trials, as well as the significant value of phenotypic big data in cotton breeding and precise cultivation. At the technical level, it introduces the application of computer vision and deep learning in plant phenotypic identification The role of machine learning methods in the prediction and classification of cotton traits, as well as the automation technology of multimodal data fusion and feature extraction. In terms of data processing and analysis, this study explored key technologies such as image segmentation and extraction of cotton plant structure parameters, time series data analysis and growth dynamic monitoring, and correlation analysis between phenotypes and genotypes as well as environmental factors. It also analyzed the practical application and effect of the AI-driven cotton phenotype platform by combining large-scale experimental cases in cotton-growing areas of China and the United States. This study looks forward to the current challenges and proposes future development trends, aiming to provide references and inspirations for future cotton phenomics research, intelligent breeding and smart agriculture. Keywords Cotton; High-throughput phenotype; Artificial intelligence; Unmanned aerial vehicle remote sensing; Field experiment 1 Introduction Cotton (Gossypium spp.) is an important economic crop related to the national economy and people's livelihood. Large-scale field trials are of great significance for screening superior varieties and optimizing cultivation measures. Every year, breeding units plant a large number of cotton varieties in different ecological zones for comparative trials to assess key agronomic traits such as yield, fiber quality and stress resistance. However, the expression of these traits is influenced by the complex interaction between genotype and environment. Accurately obtaining field phenotypic data is the core link in understanding the gene-phenotypic relationship and guiding breeding decisions. Phenotypic analysis runs through all stages of breeding experiments, from the evaluation of growth vigor at the seedling stage to the determination of yield and quality at the mature stage. It is an indispensable basis for screening high-yield, high-quality and stress-resistant varieties. Traditionally, researchers relied on manual measurement to record traits such as the height of cotton plants, the number of fruit branches, the number of bolls, and the quality of fibers. However, manual investigation is not only time-consuming and labor-intensive, but also prone to subjective biases and environmental disturbances, making it difficult to timely and comprehensively reflect the true differences of large group materials. This has made phenotypic data gradually become one of the bottlenecks restricting genetic improvement of cotton (Beegum et al., 2024). As breeding enters the era of big data, it is of strategic significance to develop highly efficient, objective and accurate phenotypic acquisition technologies. The emergence of high-throughput phenotypic analysis (HTP) technology has provided a solution to this problem. HTP rapidly monitors multiple traits related to crop growth, yield and stress resistance in the field through non-destructive means. For a long time, the field phenotypic data of cotton have mainly relied on manual observation and simple instrument measurement, such as manual measurement of plant height and bell recording, and laboratory analysis of fiber quality, etc. These traditional methods have obvious limitations: (1) Low efficiency: Manual measurement consumes a large amount of human and material resources and is difficult to cover large experimental fields in a
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