Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 145 high-chassis tractors or tracked vehicles, such as high-definition cameras, LiDAR, spectrometers, environmental sensors, etc. When the vehicle travels between crop rows, the sensors can scan the plants on both sides at close range. Because the vehicle-mounted platform is closer to the plants, the resolution and accuracy of the data obtained are often better than those of the aerial platform. For instance, a high-clearance phenotype vehicle developed by the United States is equipped with four adjustable robotic arms between cotton rows. Each robotic arm is integrated with RGB cameras and LiDAR, enabling it to obtain the three-dimensional structure of the cotton canopy and plants from multiple angles at close range. This system can measure traits such as plant height, crown width and leaf area index (LAI) throughout the entire growth period without touching the plants, and can move among tall stems, not limited by the growth of cotton and the size of the field (Jiang et al., 2018). Some vehicle-mounted phenotypic platforms have also been developed domestically. For instance, the cotton phenotypic tractor developed by the Nanjing Institute of Agricultural Mechanization can simultaneously collect multi-spectral images of the canopy and ultrasonic ranging data, enabling automatic measurement of the height and density of rows of plants. This type of platform features independent power supply and all-weather operation, making it suitable for regular field data collection Tours. The phenotypic robot is equipped with RTK differential GPS, lidar, etc., to achieve precise positioning and obstacle avoidance navigation. Because the robot is closer to the plant, it can be equipped with high-precision sensors such as microscopic imaging and close-range spectral probes to obtain information at the organ level of the crop. For instance, a certain cotton-phenotypic robot abroad uses a mechanical arm to extend into the canopy to capture high-resolution images of the leaves, and analyzes the leaf lesions and nutritional status. There are also robots installing ground spectrometers at the bottom to measure the spectra intercepted in the lower part of the cotton canopy to evaluate the light energy utilization rate of the plants (Sun et al., 2017). Of course, ground platforms also have their limitations, such as a smaller coverage area than aerial platforms and possible restrictions on movement in muddy fields. However, for experimental fields and breeding nurseries, ground platforms offer the close-up observation capabilities required for fine phenotypic measurements. Especially in the acquisition of cotton traits such as stem thickness, internode length, and the number of buds and bolls, ground platforms are more suitable. 4.3 Integration of multispectral, hyperspectral, and thermal imaging sensors The powerful functions of the high-throughput phenotypic platform cannot do without the "firepower support" of various advanced sensors. In response to the different phenotypic characteristics of cotton, the main sensors currently integrated into the platform include multispectral cameras, hyperspectral imagers, and thermal infrared cameras, etc. Each of them has its own strengths and, when working together, can capture crop information from multiple angles. A typical multispectral sensor can simultaneously obtain images in the red, green, blue (visible light), red edge, near-infrared and other bands. The vegetation indices calculated thereby (such as NDVI, EVI, etc.) are closely related to biomass parameters such as leaf area index and chlorophyll content. For instance, in the research on drought resistance of cotton, the use of multispectral unmanned aerial vehicles to obtain vegetation indices during the flowering and boll-forming period can quickly estimate the SPAD value and water content of the population leaves, providing a basis for screening drought-tolerant varieties. The experiment of Li et al. (2023) used 253 cotton varieties as materials. Under normal irrigation and drought stress conditions, the cotton canopy images were obtained by DJI Jingling 4 multispectral unmanned aerial vehicle, multiple spectral indices were extracted, and a model was established to estimate leaf nitrogen nutrition (SPAD) and water content. The results show that the multispectral index has a relatively high prediction accuracy for the key physiological indicators of cotton leaves. Multispectral cameras, due to their low cost and simple data processing, are one of the most widely used phenotypic sensors at present. They are often used to monitor growth differences, nitrogen nutrition diagnosis, and the maturity of catching, etc. Hyperspectral cameras can obtain continuous spectra in hundreds of narrow bands from visible light to near-infrared and are thus called "spectral lie detectors". Compared with multispectral cameras, hyperspectral cameras offer more abundant spectral information and can detect subtle physiological changes in plants. The hyperspectral reflectance curve of cotton leaves contains information such as chlorophyll, carotenoids, moisture,
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