Optimal Control of Greenhouse Cultivation
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Rico- Garcia , R. Peniche- Vera and G.
Herrera- Ruiz. Abstract This study aims to present recently developed applied approaches for climate control in greenhouses as well as modern trends in algorithm usage. This knowledge has been applied to the optimization of greenhouse operation. Consequently, the quality and quantity of crops have improved and a timed harvest has been made possible.
Optimal Control of Greenhouse Cultivation | Taylor & Francis Group
The use of neural networks, genetic algorithms and fuzzy logic control is also discussed. Finally, a proposal for a greenhouse climate control system based on fuzzy logic is presented; the system uses only temperature and relative humidity as inputs. Traditional agriculture in open fields is being replaced by protected cultivation in greenhouses using new automation technologies, due to the high efficiency of this approach.
Greenhouse cultivation has great potential for increased food production to meet market demands Rico-Garcia et al. Greenhouses are facilities specially built to provide favorable microclimates for optimal crop development Salazar et al. The use of pesticides is also lower than in open fields, improving product safety and quality in addition to increasing the productivity and profitability of horticultural activities.
Traditionally, greenhouse cultivation was carried out manually and included the following responsibilities:. Normally, the system programming and calibration is based on trial and error without the use of mathematical model s and precise control according to crop demands is not possible.
All growing phases of crops can be modified by the control of temperature, relative humidity, light and CO 2 in order to provide optimal conditions for growth, floration, fruit ripeness and crop health until harvest Van Straten et al. These factors may be altered by climatic control systems that manage greenhouse actuators such as heaters, coolers, motors for opening and closing windows, pumps and electrovalves. In most cases, the greenhouse control systems are central computers Morais et al. The data collected are recorded in text files and daily log files of variable measurements and actions taken are generated.
A Multi-Modelling Approach and Optimal Control of Greenhouse Climate
Computers use graphical user interfaces to display these measurements Ramirez-Rodriguez et al. A new trend in automation systems relates to the application of algorithms that control decisions based on environmental data collected by the sensors inside the greenhouse. These algorithms have the capacity to make optimal decisions and thus generate better environmental conditions according to the crop requirements, increasing quality and production. In the last several years, various strategies and intelligent control techniques have been proposed for greenhouse automation, some of which are related to models of neuronal networks Salazar et al.
Timing control: Timing control was the first system applied in greenhouses to manage actuators including irrigation pumps, motors for opening and closing windows, heaters, coolers and foggers. This system uses timers that are programmed by the greenhouse grower. The purpose is to keep a given variable within certain limits or to change it according to a predetermined program. For example, a humidity sensor reading below the allowed value cause might turn ON the nebulizer system effect to increase the relative humidity to an acceptable level or vice versa.
This type of control not only responds to high or low levels of the control variables but also takes into account a set point. However, the application of PID control to climate control inside the greenhouse presents various inconveniences due to the requirements for its implementation. The system is controlled by a transfer function that describes the system behavior, but also considers the interactions among all variables. Thus, the possibility of integrating classic controls for climate control is reduced. The main applications of classic controllers are related to the preparation of nutritive solutions in order to provide a precise dose during irrigation.
Figure 1 is the schematic diagram of a PID controller. The components of the controller include the reference input R s which represents the set point value of the control variable given by the user, the feedback H s and the error obtained with Eq. U s represents the controller output obtained using the mathematical representation of a general PID controller as shown in Eq. These algorithms are trained with data sets to yield models that describe the behavior of the variables and finally generate outputs that represent control actions or predictions about certain phenomena McCulloch and Pitts, This type of learning algorithm has recently been used to optimize greenhouse operation through the integration of so-called artificial intelligence in controllers development.
Neural networks provide a viable alternative for predictable control that can be widely used for intelligent greenhouse automation. However, their use is limited by computational costs and the large multi-dimensional sets of data that are required for algorithm training Seginer, In practice, ANN are only applied to obtain estimation models.
Abedi-Koupai et al. Trejo-Perea et al. Salazar et al. Figure 2 shows a typical neural network known as multi-layer perceptron. The network consists of processing neurons circles ; channels of information called interconnections flow between the neurons. The rectangles are neurons that simply store entrances to the network. Each processing neuron has a limited amount of memory and performs a local calculation that transforms the entries in the output.
This calculation is known as the activation or transference function of the neuron. Transference functions can be linear or non-linear and consist of algebraic or differential equations. Theoretical details of ANN can be found by Jamshidi Genetic algorithms GA : GA are optimization techniques for complex problem solving when more traditional methods cannot be efficiently applied or produce unsatisfactory solutions. GA are involved in the artificial intelligence called evolutive computation Dozier et al.
These are then modified slightly to generate descendants for the next generation. The process is repeated until a satisfactory result is achieved. The main advantages of this type of algorithm are that little knowledge is required to solve a specific problem and that they can flexibly adapt to any optimization problem.
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The disadvantage is the time required to find the solution, which depends on the population size and the number of generations. However, specialized techniques can improve GA efficiency. In greenhouses, GA are used to develop optimized models applied to enhance control actions, to contribute to the adaptation of greenhouse controllers in regions with different climates and to calibrate and adapt previously proposed climate models Hasni et al. FLC is considered when the complexity of a system precludes the application of other modeling techniques.
Due to their use of imprecise information, FLC applications tend to generate so-called expert and intelligent systems Pueyo, ; Soto-Zarazua et al. FLC has been applied in multiple engineering areas and industrial sectors Velo et al. The most recent implementations of these systems have been developed for modern low-cost platforms for embedded systems based on Digital Signal Processing DSP standards Baturone et al.
Due to its powerful mathematical foundation, FLC is a viable alternative for climate controller development that can be implemented through low cost technologies e. Finally, a general proposal for a FLC system for temperature and relative humidity inputs control for greenhouse is presented. The actual values of inputs and outputs converted in linguistic values to construct the fuzzy sets are presented in Table 1. The rule base Table 2 was integrated using the knowledge of an expert in greenhouse production.
The system was simulated using the software Matlab 6. Figure 3 and 4 show the controller behavior used to maintain the desired condition inside the greenhouse. Greenhouse automation demands new approaches to implement in new systems for greenhouse operation. All techniques must be feasible for implementing in products for the end users, i.
Greenhouse automation development also requires the development of models and control strategies that can be implemented with low cost technologies such as microcontrollers and FPGA, that allows the grower to configure the system themselves.
Because of this, we suggest that Fuzzy Logic Controls be applied. Queretaro State University. Abedi-Koupai, J.