Agricultural greenhouses greenhouse intelligent automatic control
Available online 10 February 2012
Abstract
The problem of determining the trajectories to controlgreenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.
Keywords：
Agriculture; Hierarchical systems; Process control; Optimization methods; Yield optimization
1. Introduction
Modern agriculture is nowadays subject to regulations in terms of quality and environmental impact and thus it is a field where the application of automatic control techniques has increased a lot during the last few years ( [Farkas, 2005], [King and Sigrimis, 2000], [Sigrimis et al., 2001], [Sigrimis and King, 1999] and [Van Straten et al., 2010]). The greenhouse production agrosystem is a complex of physical, chemical and biological processes, taking place simultaneously, reacting with different response times and patterns to environmental factors, and characterized by many interactions (Challa amp; van Straten, 1993), which must be controlled in order to obtain the best results for the grower. Crop growth is the most important process and is mainly influenced by surrounding environmental climatic variables (Photosynthetically Active Radiation — PAR, temperature, humidity, and CO2 concentration of the inside air), the amount of water and fertilizers supplied by irrigation, pests and diseases, and culture labors such as pruning and pesticide treatments among others. A greenhouse is ideal for crop growing since it constitutes a closed environment in which climatic and fertirrigation variables can be controlled. Climate and fertirrigation are two independent systems with different control problems and objectives. Empirically, the water and nutrient requirements of the different crop species are known and, in fact, the first automated systems were those that control these variables. On the other hand, the market price fluctuations and the environment rules to improve the water-use efficiency or reduce the fertilizer residues in the soil (such as the nitrate contents) are other aspects to be taken into account. Therefore, the optimal production process in a greenhouse agrosystem may be summarized as the problem to reaching the following objectives: an optimal crop growth (a bigger production with a better quality), reduction of the associate costs (mainly fuel, electricity, and fertilizers), reduction of residues (mainly pesticides and ions in soil), and the improvement of the water use efficiency. Many approaches have already been applied to this problem, for instance, dealing with the management of greenhouse climate in the optimal control field, e.g. Challa and van Straten (1993), Seginer and Sher (1993), Van Straten et al. (2010), Tantau (1993), or those based on artificial intelligence techniques ( [Farkas, 2003], [Herrero et al., 2008], [Martin-Clouaire et al., 1996] and [Morimoto and Hashimoto, 2000]).
The greenhouse production agrosystem has been commonly dealt with using a hierarchical control architecture ( [Challa and van Straten, 1993], Rodriacute;guez et al., 2003, [Rodriacute;guez et al., 2008] and [Tantau, 1993]), where the system is supposed to be divided into different time scales and the control system is divided into different layers to reach the optimal crop growth: a low-level layer for climate control and water and fertilizer supply (time scale of minutes), a medium-layer to control the crop growth (with a time scale of days), and a high-level layer related to market issues (time scale of months) (Rodriacute;guez et al., 2003, [Rodriacute;guez et al., 2008] and [Tantau, 1993]). However, most research on greenhouse optimal control only considers one objective in the optimization problem, mainly focused only on increasing the grower income ( [Challa and van Straten, 1993], Rodriacute;guez et al., 2003, [Rodriacute;guez et al., 2008] and [Tantau, 1993]), only minimizing the CO2 supply (van Henten amp; Bontsema, 2009), or including the thermal integral in the control decision maker (Kornera amp; Van Straten, 2008). Recently, new contributions in the optimal climate control of greenhouse crops have been published (Ioslovich, Gutman, amp; Linker, 2009) where a scheme of a seasonal optimal control policy determination based on the Hamilton–Jacobi–Bellman formalism was obtained, showing promising simulation results. On the other hand, there is only one work in literature combining more than one objective, specifically the temperature and fertilization control problems (Ioslovich amp; Seginer, 2002). However, in this work, the long-term weather predictions during the growing stage are considered constant (which can lead to a high level of uncertainty in the optimization problem). It is supposed that the temperature and nitrate profiles resulting from the optimization process are always reached (which it is not always ensured since there is a strong dependence on the current weather conditions), the water supply is not considered an objective (this being an important issue such as discussed in this paper), and only numerical examples are provided. Thus, there is no documented co
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原文：
Agricultural greenhouses greenhouse intelligent automatic control
Available online 10 February 2012
Abstract
The problem of determining the trajectories to controlgreenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.
Keywords：
Agriculture; Hierarchical systems; Process control; Optimization methods; Yield optimization
1. Introduction
Modern agriculture is nowadays subject to regulations in terms of quality and environmental impact and thus it is a field where the application of automatic control techniques has increased a lot during the last few years ( [Farkas, 2005], [King and Sigrimis, 2000], [Sigrimis et al., 2001], [Sigrimis and King, 1999] and [Van Straten et al., 2010]). The greenhouse production agrosystem is a complex of physical, chemical and biological processes, taking place simultaneously, reacting with different response times and patterns to environmental factors, and characterized by many interactions (Challa amp; van Straten, 1993), which must be controlled in order to obtain the best results for the grower. Crop growth is the most important process and is mainly influenced by surrounding environmental climatic variables (Photosynthetically Active Radiation — PAR, temperature, humidity, and CO2 concentration of the inside air), the amount of water and fertilizers supplied by irrigation, pests and diseases, and culture labors such as pruning and pesticide treatments among others. A greenhouse is ideal for crop growing since it constitutes a closed environment in which climatic and fertirrigation variables can be controlled. Climate and fertirrigation are two independent systems with different control problems and objectives. Empirically, the water and nutrient requirements of the different crop species are known and, in fact, the first automated systems were those that control these variables. On the other hand, the market price fluctuations and the environment rules to improve the water-use efficiency or reduce the fertilizer residues in the soil (such as the nitrate contents) are other aspects to be taken into account. Therefore, the optimal production process in a greenhouse agrosystem may be summarized as the problem to reaching the following objectives: an optimal crop growth (a bigger production with a better quality), reduction of the associate costs (mainly fuel, electricity, and fertilizers), reduction of residues (mainly pesticides and ions in soil), and the improvement of the water use efficiency. Many approaches have already been applied to this problem, for instance, dealing with the management of greenhouse climate in the optimal control field, e.g. Challa and van Straten (1993), Seginer and Sher (1993), Van Straten et al. (2010), Tantau (1993), or those based on artificial intelligence techniques ( [Farkas, 2003], [Herrero et al., 2008], [Martin-Clouaire et al., 1996] and [Morimoto and Hashimoto, 2000]).
The greenhouse production agrosystem has been commonly dealt with using a hierarchical control architecture ( [Challa and van Straten, 1993], Rodriacute;guez et al., 2003, [Rodriacute;guez et al., 2008] and [Tantau, 1993]), where the system is supposed to be divided into different time scales and the control system is divided into different layers to reach the optimal crop growth: a low-level layer for climate control and water and fertilizer supply (time scale of minutes), a medium-layer to control the crop growth (with a time scale of days), and a high-level layer related to market issues (time scale of months) (Rodriacute;guez et al., 2003, [Rodriacute;guez et al., 2008] and [Tantau, 1993]). However, most research on greenhouse optimal control only considers one objective in the optimization problem, mainly focused only on increasing the grower income ( [Challa and van Straten, 1993], Rodriacute;guez et al., 2003, [Rodriacute;guez et al., 2008] and [Tantau, 1993]), only minimizing the CO2 supply (van Henten amp; Bontsema, 2009), or including the thermal integral in the control decision maker (Kornera amp; Van Straten, 2008). Recently, new contributions in the optimal climate control of greenhouse crops have been published (Ioslovich, Gutman, amp; Linker, 2009) where a scheme of a seasonal optimal control policy determination based on the Hamilton–Jacobi–Bellman formalism was obtained, showing promising simulation results. On the other hand, there is only one work in literature combining more than one objective, specifically the temperature and fertilization control problems (Ioslovich amp; Seginer, 2002). However, in this work, the long-term weather predictions during the growing stage are considered constant (which can lead to a high level of uncertainty in the optimization problem). It is supposed that the temperature and nitrate profiles resulting from the optimization process are always reached (which it is not always ensured since there is a strong dependence on the
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