Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 142 accuracy of phenotypic data, while high-throughput platforms can provide a larger number of more detailed phenotypic determinations, thereby enhancing the statistical power of detecting genetic effects. For instance, by integrating big data from multi-point experiments in different environments with genotyping data, it is possible to more reliably mine major and minor QTLS and identify key functional genes. Zhao (2019) pointed out that by integrating automated platform equipment and information technology means to obtain massive amounts of multi-scale, multi-habitat, and multi-source heterogeneous plant phenotypic data, and forming plant phenomics big data, the relationship among genotype, phenotype, and environment can be systematically and deeply explored from the omics height. This means that phenotypic big data will help us gain a more comprehensive understanding of the genetic structure of important quantitative traits in cotton, such as yield and quality, providing clear gene targets for molecular breeding. In fact, the breakthroughs in cotton genomics in recent years are closely related to the large-scale collection of phenotypic data. By using high-throughput field phenotypic screening combined with genome-wide association studies (GWAS), researchers have successfully identified major genes that affect traits such as plant height and drought resistance. For instance, Ye et al. (2023) conducted GWAS using multi-time series plant height data from drones and located plant height related loci on chromosomes A01 and A11. And the candidate genes GhUBP15 and GhCUL1 were identified. These findings provide new molecular tools for cultivating ideal plant types, high-yield and stress-resistant cotton varieties. Similarly, in the improvement of fiber quality, combining large-scale phenotypic assesses (such as fiber length and strength performance under different environments) is also helpful for analyzing the genetic basis of quality traits. 3 Technical Foundations of AI-Driven Phenotyping 3.1 Applications of computer vision and deep learning in plant phenotypic recognition Computer vision (CV) is a technology that enables computers to "see" pictures and recognize objects. It has brought about significant changes in the automatic identification of plant phenotypes. In the past, the analysis of plant images mainly relied on manually designed image processing methods, such as setting color thresholds to separate green plants from soil, or using shape analysis methods to calculate the area of leaves. These traditional methods work quite well when the background is relatively simple and the target is clear, for instance, they can quickly obtain the number of plants and the coverage area. However, once in the field environment, where the light changes greatly and there are many weeds, these methods are prone to errors. In recent years, deep learning, especially convolutional neural networks (CNNS), has developed rapidly, bringing new breakthroughs to computer vision. Deep learning models can learn on their own how to recognize objects from a large number of images without the need for manual extraction of image features. In terms of plant recognition, CNN models have been applied to the detection of seedling plants, the statistics of leaf numbers, and fruit recognition, with an accuracy rate much higher than that of traditional methods (Zhang and Wang, 2024). For instance, Yang et al. (2025) used a deep learning model to identify diseases of cotton leaves, achieving a classification accuracy rate of approximately 98%. In terms of weed detection in cotton fields, deep convolutional networks can also distinguish cotton from weeds in complex backgrounds and automatically count and locate them. Deep learning can also be used to extract the phenotypic features of cotton organs. For instance, Wu et al. (2022) used images captured by drones to generate 3D point clouds, which can accurately extract structural information from cotton fields, such as plant height and canopy leaf area index. Convolutional networks can also perform image segmentation, dividing each pixel in the image into parts such as leaves, stems or bells, and then calculating the size and shape of each organ. 3.2 Role of machine learning in cotton trait prediction and classification In the era of agricultural big data, machine learning (ML) technology has become an important tool for extracting patterns from massive phenotypic data, predicting traits and classifying them. Compared with traditional statistical regression models, machine learning (especially deep learning) can handle high-dimensional nonlinear data relationships and performs well in predicting important traits of cotton. Yield forecasting is one of the most typical applications of machine learning in the phenotypic analysis of cotton. Early studies mostly adopted empirical regression models (such as multiple linear, stepwise regression, etc.) to predict per-unit yield based on indicators such as vegetation index during the growth period, but these models were difficult to fit complex nonlinear
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