Field Crop 2025, Vol.8, No.3, 139-153 http://cropscipublisher.com/index.php/fc 151 obtained remote sensing data. The second is the issue of platform adaptability. The field conditions at different test sites vary greatly, and there are different requirements for the flight of unmanned aerial vehicles and the operation of robots. Third, the challenge of model generalization. When applying machine learning models for prediction in multi-point experiments, it is often encountered that the model trained in one environment performs poorly in another. Fourth, the pressure of data management and analysis generated by large-scale multi-point trials cannot be ignored either. The high-frequency remote sensing data from dozens or even hundreds of pilot projects, when collected, form a huge database. To effectively store and manage metadata (such as variety numbers and pilot information) and provide it for researchers' use, it is necessary to establish a centralized and unified database platform. 7 Future Prospects Looking to the future from the forefront of current technological development, it can be seen that the AI-driven cotton phenotypic analysis platform is evolving towards greater intelligence, efficiency and deep integration, which will also provide strong support for the development of smart agriculture. Platform devices will be more cost-effective, automated and ubiquitous. At present, high-throughput phenotypic devices are relatively expensive and have a high usage threshold, which limits their large-scale application in the production field. One of the future development trends is cost reduction: with the large-scale production of drones and sensors, their prices will continue to decline, and the hardware investment required for AI phenotypic analysis will no longer be prohibitive. Moreover, the emergence of more open-source and low-cost components (such as open-source agricultural robot projects) will give rise to affordable versions of platforms. The development of new low-cost field intelligent phenotypic acquisition and analysis equipment will be a key focus. AI analysis algorithms will tend towards higher levels of intelligence and integrated decision-making. At present, most AI models operate independently for specific traits or tasks, such as yield prediction and disease detection. The future development trend is to build multi-task joint models or digital twin systems to achieve all-round simulation and decision support for crop growth. The deep learning model will not only tell us "what state the plant is in", but also further answer "what measures need to be taken". The AI phenotypic platform will be deeply integrated with genomics, breeding information systems and agricultural machinery operation systems, giving rise to a new type of intelligent agricultural ecosystem. In terms of breeding, the synergy of phenomics and genomics will accelerate "intelligent breeding". Algorithms trained with phenotypic big data can help breeders eliminate inferior materials at an early stage and predict unmeasured environmental performance, thereby improving the efficiency of breeding selection. From a macro perspective, the widespread application of AI-driven phenotypic platforms will bring about social and economic benefits as well as changes in scientific research paradigms. As an important economic crop, the digitalization and intelligence of cotton production will increase the output per unit area, reduce resource consumption and environmental pollution, and promote sustainable agricultural development. Of course, we also need to stay clear-headed. Intelligent technology is not omnipotent, especially in a complex field like agriculture, where AI systems sometimes make mistakes or even fail. So, in the future, it will still be necessary for humans and machines to work together. AI can handle large amounts of data, while human experts make decisions and deal with unexpected situations. For instance, AI can initially offer management suggestions, but whether to implement them in the end still depends on the agronomist's decision based on their experience and risks. This human-machine combination is very likely to become the mainstream approach in future smart agriculture. Although AI is becoming increasingly mature and people will gradually trust its judgments more, necessary human monitoring and intervention still cannot be omitted. Acknowledgments We are grateful to Dr. W. Zhang for this assistance with the serious reading and helpful discussions during the course of this work. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
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