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This paper presents a receding horizon optimal control (RHOC) method with an economic objective function for balancing the cost of resources (resource use × cost) with income through yield (yield × product price). This paper considers the two elements that determine the income through yield. The first element is yield and associated fruit development. A new, computationally viable, approach to model the income through yield is proposed and its prediction accuracy with respect to the original model is evaluated. The new approach employs a model that predicts at each time step, the future income through yield based on the assimilates partitioned to the fruits at the current time step. Simulations suggest that the assumptions made to arrive at the model for the new approach, do not significantly affect the accuracy of the predictions. The second element considered in this paper is the product price and the uncertainty inherent in its forecasts. Historical product price data are used to generate artificial product price forecasts. An uncertainty analysis, in combination with the artificial product price forecasts, showed that the product price forecast error does not considerably affect the optimised control strategy. Season-wide simulations with RHOC suggest that the product price forecast error does not considerably affect the value of the economic objective function. • A computationally viable approach to model the income through yield is proposed. • The new approach to model income through yield allows for RHOC with a short horizon. • Historical product price data are used to create artificial product price forecasts. • Product price forecast errors do not considerably affect the control strategy. [ABSTRACT FROM AUTHOR]

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on "autonomous greenhouses" aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference. [ABSTRACT FROM AUTHOR]

LED lighting is appointed as the successor of HPS lighting in greenhouses since it can lead to a more sustainable cultivation, i.e. it converts electrical energy into photosynthetically active radiation more efficiently. To quantify the effect of this more efficient conversion within the operation of the greenhouse system, an optimal controller is proposed to generate optimal control trajectories for the controllable inputs of the greenhouse. The optimal controller makes use of an economic objective function, i.e. the difference between income (y i e l d × p r o d u c t p r i c e) and cost of resources (r e s o u r c e u s e × c o s t). The performance of this optimally controlled greenhouse system is compared with respect to the state-of-the-practice. Simulation experiments suggest optimal control can increase the economic objective by 10 % to 65.14 €. m − 2 compared to 58.96 €. m − 2 for the state-of-the-practice, for tomatoes cultivated in a Dutch weather conditions. The model of the optimally controlled greenhouse is used to compare the performance of different lighting systems, i.e. no lighting, HPS lighting and LED lighting. An increase of 9 % in the operational return is observed for LED lighting compared to HPS lighting. The electricity that is saved due to the more energy-efficient conversion in the LED lighting results in a 30 % decrease in carbon footprint when comparing a greenhouse with LED lighting to a greenhouse with HPS lighting. • A model suitable for the control of the greenhouse climate is developed. • The model allows for the optimization of a purely economic cost function. • Optimal control increases the return by 10% compared to the state-of-practice. • Simulations suggest a 9 % increase in return when using LED lights instead of HPS. • Carbon footprint decreases by 30 % when using LED lights instead of HPS. [ABSTRACT FROM AUTHOR]

An optimisation algorithm, as an essential part of a model-based method to design greenhouses for a broad range of climatic and economic conditions, was described. This algorithm – a modified controlled random search using parallel computing – maximised the annual Net Financial Return (NFR) for a tomato grower by selecting the best alternative to fulfil eight design elements: type of greenhouse structure, material of the cover, outdoor shade screen, whitewash properties, thermal screen, heating system, cooling system and CO2 enrichment system. As an example, the algorithm was applied to two locations with different climatic and economic conditions, Almeria and The Netherlands. Due to the warm climate with high radiation levels in Almeria, a greenhouse with a relatively large specific ventilation area (20% compared to 14% for Dutch conditions), seasonal whitewash and a low-capacity direct air heater (50 W m−2 compared to 200 W m−2 for Dutch conditions) was selected. In contrast, for the relatively cold climate with low radiation levels of the Netherlands, a 100% aluminium thermal screen and no whitewash would give the best result. The design method produced realistic greenhouses and related annual NFR, indicating that the method performs well. An analysis of the close-to-best greenhouses showed that, for both locations, a structure with high light transmissivity considerably enhanced the greenhouse performance whereas an outdoor shade screen, geothermal heating and mechanical cooling would be not economical. These results demonstrate the feasibility of a model-based design approach that produces suitable greenhouse designs for given climatic and economic conditions. [ABSTRACT FROM AUTHOR]