Rule-based year-round model predictive control of greenhouse tomato cultivation: A simulation study

Abstract

Maximizing profit is usually the objective of optimal control of greenhouse cultivation. However, due to the problem of “the curse of dimensionality”, the global optimization of greenhouse climate is usually difficult when faced with a complex dynamic model and a long cultivation period. Compared with leafy vegetables with a much simpler dynamic model and a much shorter cultivation period, the year-round tomato model usually has many more states to describe its dynamics better. To solve the year-round climate control of greenhouse tomato cultivation, a rule-based model predictive control (MPC) algorithm is raised. The innovation of this paper lies in that the setpoints of the proposed MPC algorithms are determined by the external weather and the month-averaged predictions of the tomato price. With the greenhouse climate – tomato growth dynamic model and the economic performance index, different MPC algorithms are compared with the traditional on/off control algorithm and the open field cultivation. Quantified results of yield, cost, and profit are obtained with the weather data and market data collected in Beijing. Findings of this paper showed that the year-round greenhouse tomato cultivation in Beijing is hardly profitable with the tomato price sold as an open field product (XFD price). With the tomato price sold as a high-tech greenhouse product (JD price), the higher yield guarantees a higher profit. Moreover, the simple emphasis on energy minimization cannot even guarantee a higher yield than that in the open field. A synthetical consideration of yield and cost is a prerequisite for a high profit.