Research Progress on Remote Sensing Monitoring and Intelligent Decision-Making Algorithms for Rice Production

[Significance] Rice is a staple food crop worldwide, and ccurate monitoring of its growth is crucial for global food security. Remote sensing serves as a powerful tool in modern agriculture. By integrating remote sensing with intelligent decision-making algorithms, farmers can achieve more precise and sustainable rice cultivation. To provide actionable insights and guidance for researchers in this field, this review examines the latest advancements in remote sensing and smart algorithms for rice farming, while addressing current challenges and future trends. [Progress] Currently, remote sensing-based monitoring systems for rice production have been comprehensively implemented across the entire production cycle. For planting distribution identification, optical remote sensing and synthetic aperture radar (SAR) technologies complement each other to enhance accuracy through data fusion. Regarding growth period monitoring, a robust technical framework has been established, incorporating the empirical threshold method, shape model approach, and machine learning classification techniques. Dynamic evaluation of growth status is enabled by constructing correlation models between remote sensing features and biophysical parameters. Disaster monitoring systems provide rapid responses to various natural disasters. Yield and quality predictions integrate crop models, remote sensing data, and machine learning algorithms. Intelligent decision-making algorithms are deeply embedded in all stages of rice production. For instance, during planting planning, the integration of geographic information systems (GIS) and multi-criteria evaluation methods facilitates regional suitability assessments and farm-level quantitative designs. In topdressing management, nitrogen-based intelligent algorithms have significantly improved fertilization precision. Irrigation optimization achieves water conservation and emission reduction by synthesizing soil moisture and meteorological data. Finally, precise pesticide application prescriptions are generated using remote sensing and unmanned aerial vehicle (UAV) technologies. [Conclusions and Prospects] Despite significant progress, current research faces persistent challenges, including difficulties in multi-source data fusion, complexities in acquiring prior knowledge, insufficient model standardization, and barriers to large-scale technology implementation. Future efforts should prioritize the following six directions: (1) Technological innovation: Advance collaborative analysis of multi-source remote sensing data, design optimized data fusion algorithms, and construct an integrated air-space-ground monitoring network; (2) Intelligent algorithms: Explore cutting-edge techniques such as generative adversarial networks (GANs) and federated learning to enhance model adaptability across diverse environments; (3) Research scale: Establish a global rice growth monitoring system and develop multi-factor coupling models to assess climate change impacts; (4) Technology dissemination: Strengthen demonstration projects, reduce equipment costs, and cultivate interdisciplinary professionals; (5) Standards and protocols: Promote internationally unified standards for monitoring and decision - making frameworks; (6) System integration: Leverage technologies such as digital twins and blockchain to develop smart agriculture platforms for end-to-end intelligent management. Through multi-dimensional innovation, these advancements will significantly elevate the intelligence of rice production, offering robust support for global food security and sustainable agricultural development.