Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 144 major traits based on experience for regression. This kind of manual feature engineering is both time-consuming and may miss key information. Deep learning models can automatically learn multi-level features from raw data, such as automatically extracting the shape and texture features of disease spots or the spectral features of chlorophyll content from leaf images. Especially in multimodal data scenarios, automatic feature extraction has more advantages-the model can comprehensively consider various data modalities and extract useful information at different scales. For instance, CNN can simultaneously process canopy images of cotton at different heights, automatically capturing the growth characteristics of different growth stages through convolutional layers without the need to manually define which period or which index is most relevant. 4 High-Throughput Phenotyping Platforms and Sensor Integration 4.1 Advantages of UAVs and satellite remote sensing for large-scale cotton phenotyping In recent years, unmanned aerial vehicles (UAVs) and satellite remote sensing technologies have been widely applied as high-throughput phenotypic data acquisition methods in cotton research. The unmanned aerial vehicle (UAV) remote sensing platform has outstanding advantages such as mobility, high resolution and easy operation, and is particularly suitable for phenotypic monitoring of cotton in large fields. Firstly, drones can fly at low altitudes to obtain canopy images with centimeter-level resolution, clearly presenting details at the level of individual plants and even organs, such as leaf color, the number of flowers and the degree of catl release, which is much higher than the resolution of satellite images. Studies show that the RGB images of unmanned aerial vehicles can accurately extract the structural parameters of cotton plant height by reconstructing three-dimensional point clouds, and the correlation with the measured ground height reaches more than 0.95. Psiroukis et al. (2023) utilized visible light images from unmanned aerial vehicles (UAVs) to generate digital surface models for estimating the height of cotton plants. The results were highly consistent with those measured manually (R²> 0.90), verifying the accuracy and reliability of UAV measurements. Secondly, unmanned aerial vehicles (UAVs) respond quickly and can flexibly adjust flight time and frequency according to demand, thereby achieving multi-temporal dynamic monitoring of phenotypic traits. For instance, aerial photography of the experimental fields can be conducted every week or even every few days to record the growth curves of cotton and key growth turning points, which is difficult to achieve through traditional manual operations. Secondly, the coverage range of the unmanned aerial vehicle (UAV) is moderate. A multi-rotor UAV can easily obtain data from dozens to hundreds of hectares of experimental fields in a single day, making it particularly suitable for large-scale experiments at research sites or breeding bases. In contrast, although satellite data has a larger coverage area, it is often limited by spatiotemporal resolution and affected by cloudy and rainy weather. However, drones can take off and land at any time on fine days to obtain clear images. Practice has proved that in key agronomic links such as cotton defoliation and ripening, unmanned aerial vehicle (UAV) monitoring has played an irreplaceable role. Ma et al. (2021) utilized drones equipped with RGB cameras to capture images of cotton fields before and after defoliation in mechanical harvesting, and rapidly calculated the defoliation rate through vegetation indices, providing an efficient means for evaluating the effectiveness of defoliants. The model it established has shortened the manual investigation time from several days to just a few minutes, and the monitoring accuracy of the deleafing rate has reached over 90%, greatly improving the efficiency of related experiments on mechanical cotton harvesting. Satellite remote sensing has unique advantages in large-scale area monitoring and long-term sequence data. Satellite platforms (such as the European Sentinel-2 and the US MODIS, etc.) can cover the entire major cotton-producing areas and provide regional-scale vegetation growth and yield estimation information. Although satellite images have a relatively low resolution (typically 10 m to 30 m), making it difficult to analyze individual plant information, they have a wide coverage and a fixed period, making them suitable for macroscopic analysis. 4.2 Applications of ground-based phenotyping platforms (automated vehicle systems, robotics) In addition to aerial platforms, ground-based high-throughput phenotypic platforms are also a current research hotspot, offering advantages in precisely capturing plant details and collecting data around the clock. Ground platforms mainly include two types: modified high-clearance vehicles (phenotype tractors) and field autonomous robots. The automatic vehicle-mounted phenotypic system is usually equipped with a variety of sensors on
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