Crop growth simulation irrigation optimization method and system based on data assimilation

By employing data assimilation and multi-scale climate prediction-based irrigation optimization methods, the stability of irrigation systems under extreme weather conditions has been addressed, enabling more scientific and flexible irrigation decisions and improving the efficiency of irrigation water resource utilization and the stability of agricultural production.

CN122155001APending Publication Date: 2026-06-05ZHENGZHOU UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHENGZHOU UNIV
Filing Date
2026-02-09
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing irrigation optimization systems are unstable under extreme weather conditions and cannot effectively cope with weather uncertainties, resulting in poor stability of irrigation schemes during actual implementation and frequent need for manual intervention.

Method used

A crop growth simulation method based on data assimilation is adopted. The model is updated by real-time observation data. Combined with multi-scale climate prediction and rolling time window optimization decision-making, an irrigation plan is generated. Multi-objective optimization algorithm and emergency response mechanism are introduced to dynamically adjust irrigation strategy.

Benefits of technology

It improves the robustness and adaptability of irrigation systems under extreme weather conditions, ensures the scientific and flexible nature of irrigation decisions, and enhances the efficiency of irrigation water resource utilization and the stability of agricultural production.

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Abstract

The application discloses a crop growth simulation irrigation optimization method and system based on data assimilation, and belongs to the field of intelligent agriculture and precise irrigation technology.The method dynamically calibrates a crop growth model set by assimilating remote sensing and ground observation data through algorithms such as Kalman filtering, ensures that the model state is consistent with the actual field, fuses multi-scale climate prediction data of ultra-short-term, short-term and medium and long-term, and generates comprehensive weather prediction results according to the dynamic allocation of weights based on historical performance, timeliness, consistency and crop key requirements; in a rolling time window, the optimal irrigation strategy is solved by using an optimization algorithm with yield, water use efficiency and economic benefit as multi-objectives, and only the recent plan is executed; the state and prediction data are iterated based on the updated state after execution, and an extreme weather emergency response mechanism is introduced. The application improves the model simulation accuracy and decision reliability, and realizes dynamic optimization of water resources.
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