Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 150 seasonal meteorological data, and mid-growth drone images. By fusing these heterogeneous data through the CNN model for training, an accurate prediction of the experimental yield was achieved. The results show that the prediction accuracy of the fusion model is significantly higher than that of using only a single data source, demonstrating the powerful ability of multi-source AI models in cotton yield prediction. Figure 2 Summary of measured chlorophyll (Chl) and spectral reflectance data collected from cotton leaves during field studies at Maricopa, Arizona, USA, including (A) area-basis Chl, (B) mass-basis Chl, and (C) the minimum, median, and maximum of spectral reflectance data from the 2019-2020 experiment and (D) area-basis Chl, (E) mass-basis Chl, and (F) the minimum, median, and maximum of spectral reflectance data from the 2021-2022 experiment (Adopted from Thorp et al., 2024) 6.3 Performance and challenges of AI phenotyping platforms in multi-location trials Multi-location Trials are not new. They have always been an important method for evaluating the stability and adaptability of cotton varieties. It's just that now with the support of AI, the situation is a bit different. On the one hand, AI-driven phenotypic platforms have indeed significantly improved the efficiency of experiments and the quality of data. However, on the other hand, not all problems have been solved, and challenges have also emerged. Take data acquisition as an example. In the past, during national cotton regional trials, many pilot projects could only record a few indicators-yield, quality, and a few basic agronomic traits. Nowadays, some places have introduced unmanned aerial vehicle (UAV) monitoring. Not only is the efficiency higher, but the data obtained is also more detailed. For instance, the vegetation index and plant height at different time points throughout the entire growth period can all be observed. This additional information can precisely help us understand where the yield differences among varieties come from. For instance, in the cotton-growing areas of North China, a regional trial discovered an interesting phenomenon: the NDVI of two high-yield new varieties at the end of flowering was significantly higher than that of the control variety, indicating that their leaves could still function well in the later stage and did not decline prematurely. Sure enough, the final result also confirmed this point-not only were the single bells of these two materials heavy, but the number of knots was also large. In contrast, for those materials with low yields, NDVI drops rapidly after flowering and ages significantly earlier (Gu et al., 2024). However, in practice, it has also been found that multi-site trials have brought some challenges to the operation of the AI phenotypic platform. The first issue is data standardization. The environmental background, lighting conditions and operation methods of different test sites may vary, resulting in systematic deviations in the
RkJQdWJsaXNoZXIy MjQ4ODYzNA==