Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 146 and phenology. By analyzing the characteristic bands or spectral indices, the nutritional and stress conditions can be evaluated more accurately. For instance, a study by Thorp et al. (2024) deployed a field hyperspectrometer to monitor the reflectance of cotton leaves. By combining 148 spectral indices and multiple machine learning methods, the chlorophyll content of the leaves was predicted. As a result, the R2 of the best model reached 0.88. Hyperspectral remote sensing is also used for the early detection of cotton diseases. The temperature of leaves can actually reveal a lot about their "internal conditions". Like thermal infrared cameras, they determine whether crops are short of water and how fast they are evaporating by detecting the radiation temperature on the surface of the canopy. Generally speaking, when there is less water or the stomata stop opening, the temperature of the leaves will rise rapidly. Therefore, the canopy temperature has become an "indicator light" for judging the degree of water stress and irrigation requirements of cotton. However, not everyone uses this method from the very beginning. Nowadays, it is popular to use drones in combination with thermal imaging technology to fly around the fields to see where the temperature is high and there may be a lack of water, and then decide whether to replenish water and how much to replenish. In some studies, this method has been used quite maturely. O'Shaughnessy et al. (2023) conducted an AI-driven irrigation experiment, where they combined thermal imaging with Internet of Things (iot) sensors to optimize the irrigation strategy. The result was also quite impressive-20% to 35% of water was saved, but the output did not drop. It seems that water conservation is really not something that can be accomplished on a whim. 5 Applications of AI-driven Phenotyping Platforms in Data Processing and Analysis 5.1 Image segmentation and extraction of cotton plant structural parameters Image segmentation is the process of dividing an image into several regions of interest. In the phenotypic analysis of cotton, a typical task is to separate the background soil, weeds and cotton plants, and further divide the plants into different organs (leaves, stems, bolls, etc.). The traditional threshold segmentation method is feasible in simple backgrounds, but its accuracy is not high when facing complex field backgrounds. Deep learning provides more robust semantic segmentation schemes. Networks such as U-Net and Mask R-CNN can learn the shape and texture features of cotton leaves and bell shells using artificially labeled training data, and can accurately outline the plant contusions and organ regions even in complex backgrounds. For instance, some studies have used Mask R-CNN to perform instance segmentation on cotton floss images during the floss opening period, which can separate each floss from the background and count it, providing a basis for evaluating the floss opening rate and the timing of harvest (Adke et al., 2022). The results of image segmentation can also be used to calculate plant type parameters, such as canopy coverage (proportion of green pixels), projected area, etc., as indicators of vegetation growth. For the division and identification of high-density plants, deep learning segmentation combined with connected domain analysis technology can automatically count the number of seedlings emerging in the field and the seedling spacing, which is faster and more accurate than manual counting. After the image segmentation is completed and a clean cotton plant image is obtained, some structural parameters can be extracted. One of the most common parameters is the plant height. In the past, when measuring the height of a plant, a ruler was usually used, measuring from the ground to the top of the plant. This method was relatively slow and prone to errors. Nowadays, three-dimensional reconstruction technology can be used to measure the height and structural data of the plant without touching it. For instance, after processing the low-altitude images captured by drones using the Structure from Motion (SfM) algorithm, high-density point clouds of the experimental field can be generated. By subtracting the Digital Ground Model (DEM) from the Digital Surface Model (DSM), the average plant height of each plot can be calculated. Another common structural parameter is the Leaf Area Index (LAI), which indicates the number of leaf areas per unit of ground. LAI can reflect the growth condition of cotton and its ability to absorb light energy. In the past, to measure LAI, it was generally done by direct measurement or with instruments (such as LAI-2200). Nowadays, LAI can also be estimated by combining AI platforms with remote sensing technology. In the study by Wu et al. (2022), they used a point cloud model generated by a drone to not only monitor the changes in cotton plant height but also estimate the LAI after defoliation treatment. The research found that three days after the application of defoliant, the R²between the LAI estimated by point cloud and the measured value reached 0.872. This indicates that as long as there are
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