水稻生产遥感监测与智慧决策研究进展

doi:10.12133/j.smartag.SA202501002

Smart Agriculture ›› 2025, Vol. 7 ›› Issue (2): 57-72.doi: 10.12133/j.smartag.SA202501002

• 专题--粮食生产大数据平台研发与应用 • 上一篇下一篇

水稻生产遥感监测与智慧决策研究进展

赵柄婷¹^,²^,³^,⁴, 华传海¹^,²^,³^,⁴, 叶晨洋¹^,²^,³^,⁴, 熊育春¹^,²^,³^,⁴, 钱涛¹^,²^,³^,⁴, 程涛¹^,²^,³^,⁴^,⁵, 姚霞¹^,²^,³^,⁴^,⁵, 郑恒彪¹^,²^,³^,⁴^,⁵, 朱艳¹^,²^,³^,⁴^,⁵, 曹卫星¹^,²^,³^,⁴^,⁵, 江冲亚¹^,²^,³^,⁴()

^1. 南京农业大学国家信息农业工程技术中心，江苏南京 210095，中国
^2. 智慧农业教育部工程研究中心，江苏南京 210095，中国
^3. 农业农村部农作物系统分析与决策重点实验室，江苏南京 210095，中国
^4. 江苏省信息农业重点实验室，江苏南京 210095，中国
^5. 现代作物生产省部共建协同创新中心，江苏南京 210095，中国

收稿日期:2025-01-01 出版日期:2025-03-30
基金项目:
国家重点研发计划项目(2023YFD2000103)
作者简介:
赵柄婷，博士研究生。研究方向为水稻生产遥感监测。E-mail：2024201127@stu.njau.edu.cn
通信作者:
江冲亚，博士，教授，研究方向为农情遥感监测。E-mail：chongya@njau.edu.cn

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

ZHAO Bingting¹^,²^,³^,⁴, HUA Chuanhai¹^,²^,³^,⁴, YE Chenyang¹^,²^,³^,⁴, XIONG Yuchun¹^,²^,³^,⁴, QIAN Tao¹^,²^,³^,⁴, CHENG Tao¹^,²^,³^,⁴^,⁵, YAO Xia¹^,²^,³^,⁴^,⁵, ZHENG Hengbiao¹^,²^,³^,⁴^,⁵, ZHU Yan¹^,²^,³^,⁴^,⁵, CAO Weixing¹^,²^,³^,⁴^,⁵, JIANG Chongya¹^,²^,³^,⁴()

^1. National Engineering Technology Research Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
^2. Engineering Research Center for Smart Agriculture, Ministry of Education, Nanjing 210095, China
^3. Key Laboratory of Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
^4. Key Laboratory of Information Agriculture, Jiangsu Province, Nanjing 210095, China
^5. Modern Crop Production Provincial and Ministry Collaborative Innovation Center, Nanjing 210095, China.

Received:2025-01-01 Online:2025-03-30
Foundation items:National Key Research and Development Program of China(2023YFD2000103)
About author:
ZHAO Bingting, E-mail: 2024201127@stu.njau.edu.cn
Corresponding author:
JIANG Chongya, E-mail: chongya@njau.edu.cn

摘要/Abstract

摘要：

【目的/意义】 总结叙述水稻生产遥感监测及智慧决策算法的研究进展，剖析面临的挑战，展望未来方向。 【进展】 首先介绍了遥感手段在时空尺度上如何监测水稻生长，其次剖析了遥感在水稻种植分布、生育期监测、长势监测和产量品质估算等方面的应用；最后阐述了水稻生产智能决策算法与遥感耦合的潜力。 【结论/展望】 指出了遥感手段在水稻生产监测与人工智能耦合的应用与机遇，提出了大尺度、实时监测水稻生长的挑战与展望。本文可为水稻生产遥感监测相关深入研究提供参考，推动水稻精准生产和农业可持续发展。

关键词: 水稻生产, 遥感, 产量模拟, 决策算法, 智慧农业

Abstract:

[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.

Key words: rice production, remote sensing, yield simulation, decision algorithms, smart agriculture

中图分类号:

S511
TP79

赵柄婷, 华传海, 叶晨洋, 熊育春, 钱涛, 程涛, 姚霞, 郑恒彪, 朱艳, 曹卫星, 江冲亚. 水稻生产遥感监测与智慧决策研究进展[J]. 智慧农业(中英文), 2025, 7(2): 57-72.

ZHAO Bingting, HUA Chuanhai, YE Chenyang, XIONG Yuchun, QIAN Tao, CHENG Tao, YAO Xia, ZHENG Hengbiao, ZHU Yan, CAO Weixing, JIANG Chongya. Research Progress on Remote Sensing Monitoring and Intelligent Decision-Making Algorithms for Rice Production[J]. Smart Agriculture, 2025, 7(2): 57-72.

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水稻生产遥感监测与智慧决策研究进展

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

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