Optimal Irrigation Allocation for Large-Scale Arable Farming
IEEE Transactions on Control Systems Technology(2022)
摘要
In this article, we propose an optimization framework that computes the allocation of irrigation machinery (agents) and water to arable fields by maximization of a profit function in a receding horizon fashion using realistic models for crop growth dynamics. A key advantage of the proposed framework is that it can use many of the existing crop growth models for prediction and hence can be applied to a wide variety of crops, soils, locations, and weather patterns. The output of the framework is a feasible allocation of agents to fields and delivery of water over the growing season, in such a way that it allows for practical implementation by farmers. The allocation is feasible as the framework takes into account relevant real-world constraints such as the water-carrying capacity and application rates of the agents, but also traveling costs and refilling of water at designated locations. A realistic case study using validated crop-growth models by agronomists is used to show the strengths and the generality of the framework.
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关键词
Large-scale systems,model predictive control (MPC),multi-agent systems,precision agriculture
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