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Trends in Automated Systems Development for Greenhouse Horticulture
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. 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.
... where U(s) is the control variable while U max and U min are the maximum and minimum limits of the control operation and e represents the error [7]. ...
... The on-off controller most frequently used controller in greenhouse automation where it is based on cause and effect in independent loops. 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) [7]. ...
... FLC has been applied in many engineering areas and industrial sectors including bioprocess, greenhouse climate control and automatic feeders for aquaculture [7]. 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 and FPGA [9]. ...
The automated irrigation concept is not new, human has figured out on how to irrigate large areas of foliage through the use of automated irrigation systems. Currently, automated irrigation systems which can irrigate plants to a desired level and supply plants with amount of water required for normal plant growth are not available. Indeed, if these systems can be developed, it could reduce the waste of water. Nowadays, computerized control is very appreciated for the greenhouse irrigation control. Conventional method such as on-off control method or proportional control method basically results in a loss of energy and productivity. In order to maximize the efficiency and production for irrigation system, the fuzzy logic controller is proposed to estimate the amount of water of plants in distinct depth using the irrigation model, soil type, environmental conditions of greenhouse and type of plant that affecting the greenhouse irrigation system. As per discussion, this paper presents a solution for irrigation controller and comparison method of on-off controller method and fuzzy logic controller method using MATLAB software.
... The major control algorithms used for greenhouse operation are timing, ON-OFF and PID control. Currently, so many control algorithms gave been proposed for greenhouse automation, some of which are related to adaptive and optimal control [fuz], fuzzy logic [9,10], neural networks [11], genetic algorithms [12]. This paper discusses the applications of wireless sensor network technology for real-time monitoring and control of selected variables in greenhouse. ...
... Fuzzy logic control is a powerful tool to deal with ill-defined nonlinear systems [9]. In this paper, a rule-based fuzzy controller has been proposed to regulate the internal climate of the greenhouse. ...
The main objective of this research is to design and implement a real-time monitoring and control of several environmental parameters for group of greenhouses. Each greenhouse is considered as a node in a wireless sensor network. A single-board microcontroller-based system has been designed and implemented to monitor and control several variables and maintain desired condition in each greenhouse. A rule-based fuzzy controller has been designed to control the microclimate of each greenhouse. The proposed system enables the farmer to monitor both the internal environment of the greenhouse. Also, the farmer can send commands to turn ON or OFF certain devices in a selected greenhouse through wireless communications. Simulated and real results have been achieved to demonstrate the system performance and real-time remote monitoring and control activities.
... Some ACSs are developed to control the climate variable such as temperature, humidity and CO 2 concentration. Data processing systems and sensors are connected through communication protocols, including ModBus and RS-232 [11], [12]. These systems are used to control the greenhouse environment, but there is a lack of optimization techniques to minimize energy consumption and prefer the user-desired parameters. ...
Greenhouses are a productive system that allows us to respond to the growing global demand for fresh and healthy food throughout the year, but the greenhouse environment is not easily controlled because its climate parameters are interrelated. However, the numbers of the actuator are operated parallelly to maintain the greenhouse environment; as a result, the energy consumption of greenhouses is high. In this study, we presented the optimization module by considering the outdoor environment with the aim of minimum energy consumption. Metaheuristic-based Differential Evaluation (DE) is used to optimize the climate parameters by considering indoor and outdoor environmental constraints. Furthermore, the Long Short-Term Memory (LSTM) based inference model is offloaded on the Internet of Things (IoT) device to predict the next environmental situation. The objective function selects the optimal parameters within user preferences with minimum energy consumption based on the inferred parameter value. The open-source software framework IoTivity, implementing Open Connectivity Foundation (OCF) technical standards, is used for the real-time connection between IoT devices and the IoT platform. Greenhouse owners can set the preferences based on the requirements of plants in the greenhouse by using a smart and remotely accessible Android-based interface. A fuzzy logic-based control module operates on an IoT device that maps the optimized parameters with the actuator and operates accordingly. The proposed model is analyzed, and the performance is evaluated in terms of energy consumption for each climate parameter and actuator in the greenhouse. The results show that the proposed mechanism saves 36% of energy.
... The base station is able to modify weather conditions according to the preset parameters of the desired plant to be grown. ESP8266-based wireless monitoring and control system for greenhouses is designed of a base station and front-end devices including sensors and electrical actuators organized as a star network shown in Fig. 18.1 [2][3][4][5][6]. ...
The Internet of Things plays role in all arenas. In this manuscript, we have to consider the growing flowers in a garden, vegetable, fruit, and other farming. We are considering the greenhouse which aims to introduce the productions of yields. Of course, the growth of plants, and farms are vital and need of everyone, keeping in view of this manuscript is aimed to discuss and study in line of IoT and agriculture. In this work, we propose a greenhouse automation system based on Arduino for the monitoring of temperature, humidity, and moisture of the soil. Arduino can obtain data on the environmental conditions of the greenhouse from various sensors and transfer the data to the ESP8266 module. Consequently, it's possible to change the state of greenhouse control devices like fans, lamp heater, and water pump in obedience to the necessary conditions of the crops. These parameters are modified by the type of plant to maximize their growth, the Aloe Vera plant was used in this project. For the architecture of the Internet of Things was used Blynk coming from the embedded board and the communication link with the Blynk Server was through the Wi-Fi protocol. Results indicate that the system allows the control and monitoring in real-time of the greenhouse correctly. As a future improvement, it is intended with the data obtained, to search for the best optimal conditions for plant growth through artificial intelligence.
... The greenhouse climate is closely monitored and monitored around the clock through the use of a remote base station [11]. The success of protected agriculture as an alternative to traditional agriculture has been demonstrated by the in-troduction of automation and artificial intelligence techniques in greenhouses [10][11][12]. This paper presents a smart algorithm for the processing, detection and evaluation of sensor readings and the method of transmitting the information in one intelligent sensor module and evaluate it performance within a wireless sensor network. ...
The primary processing of data collected in the sensor node is a major aspect of the performance of wireless sensor networks. This paper provides an experimental description of a smart algorithm in which the sensitivity node operates. The specification is based on measuring the current that is discharged from the power source in data transmissions. The measurements allow for the definition of an analytical model for the number of data packets to be sent from the terminal sensor node, a function of sensor operation and the level of change of sensor data. Conventional sensor nodes are not effective in terms of productivity, flexibility, energy consumption and work interference. The sensor node is programmed according to the conventional method, which sends the data every minute, and then reprogrammed according to the intelligent algorithm in which the transmitter unit is activated when there is a difference between the averages of ten readings in one minute with the previous average reading. Temperature and humidity were measured over a 12-hour period. The conventional sensor node needed to send 60 packets of data within an hour, which could be repeated data. The use of the smart node algorithm improved current consumption by 93% compared to the commercial node. The integration of the wireless sensor node with the smart algorithm has several advantages, such as the number of packet data sent is lower and of high accuracy, the total power consumed for the node is lower. In addition to the possibility of increasing the number of sensor nodes in the wireless sensor network.
... The greenhouse automation for the previously described activities using optimized process technologies is the next natural step (Oliveira et al., 2017;Soto-Zarazua et al., 2011), in order to reduce the human labor, the energy and materials, and to improve the quality and production of greenhouse facilities, i.e., to increase the performance/cost index. The optimized resources are: water, energy, materials, labor, space and capital. ...
This paper reviews the recent developments and implementations for greenhouses facilities, focusing on recent progress regarding greenhouse environment monitoring and control with many available application examples. State of the art and current trends concerning the main parts of the greenhouse environment automation are discussed: i) greenhouse climate models, ii) wireless sensor networks, iii) remote monitoring/command and supervisory control and data acquisition (SCADA) systems, iv) image processing. The greenhouse engineering covers multi-disciplinary approach, engineering and economics, and for final success and sustainability, social and political support must also be achieved. Greenhouse complex nonlinear coupled climate and biological models are discussed with high importance in greenhouse optimal control solutions. Greenhouse monitoring and control applications using Wireless Sensor Networks (WSN) ZigBee modules, GPRS data transmission, and CAN bus communication are presented and classified, highlighting the communication specific benefits. Remote monitoring/command and supervisory control and data acquisition (SCADA) systems are analyzed and classified offering to users the following advantages: local and remote visualizations of process data, access to process set-points, optimal control strategies, database data recording, report generations and alarm management. Image processing is another development direction for greenhouse facilities, with promising results for insect monitoring, chlorophyll content estimation, identification, classification and harvesting of fruits. The analysis and classification in appropriate categories of recent contributions, using significant application examples referred in the paper, offers a vision for the greenhouse environment monitoring and control based on modern solutions and technologies, ideas for new applications and relevant research opportunities to optimize the greenhouse processes.
... De acuerdo a la Organización de las Naciones Unidas para la Agricultura y la Alimentación (FAO) las ventajas del uso de invernadero son: protección contra condiciones climáticas extremas, control de temperatura, de iluminación, concentraciones de CO 2 , producción en cualquier temporada del año, mejorar la calidad del cultivo, preservación de la estructura del suelo, aumento considerable de la producción, ahorro en costos de producción, disminución en el uso de plaguicidas, aprovechamiento del área de cultivo, uso racional del agua, mayor periodo de producción, entre otras. Del mismo modo, las nuevas tecnologías automatizadas ayudan al productor en las operaciones bajo invernadero (Soto-Zarazúa et al., 2010). ...
La principal muestra de calidad en el fruto de jitomate es presentar un estado de madurez homogéneo en cada etapa de desarrollo; a su vez, la cantidad del carotenoide licopeno es mayor conforme el estado de maduración aumenta. Conocer y analizar precisa y oportunamente estos dos factores, nos llevan a generar métodos de análisis mediante procesamiento de imágenes. Además de proporcionarnos un método de análisis no invasivo, es posible obtener resultados en tiempo real. En éste trabajo se presenta una metodología para segmentar imágenes de jitomates utilizando el espacio de color CIELAB para posteriormente dejar la imagen preparada para un análisis detallado y poder estimar el grado de madurez y cantidad de licopeno en frutos de jitomate. Palabras clave: Procesamiento de imágenes, segmentación, madurez, licopeno, jitomate. 1. INTRODUCCIÓN El jitomate (Licopersicum esculentum) es uno de los frutos más populares y ampliamente cultivado en el mundo. Actualmente es consumido en la dieta diaria, ocupando un importante lugar en el consumo mundial de productos hortícolas (Candelas et al., 2006). Los tomates son la mayor fuente de antioxidantes. Son un fruto de temporada y su disponibilidad está limitada durante ciertas temporadas del año (Gastélum-Barrios et al., 2011). Diferentes atributos como el color, la madurez, la firmeza, el tamaño, la forma y la composición determinan su calidad (Jaramillo et al., 2007). Una importante cantidad de investigaciones se han desarrollado para relacionar la posibilidad de prevenir ciertos tipos de cáncer y problemas cardiovasculares, con el consumo de productos derivados del jitomate (Muratore et al., 2008). La firmeza y el color son atributos importantes para determinar la madurez del fruto tanto para consumo en fresco como procesado. Un fruto con una madurez homogénea es deseado tanto por el consumidor final como para el productor. Por otro lado, los carotenoides son nutrientes que actúan como antioxidantes y son los responsables del color rojo en el fruto de jitomate. Entre los carotenoides se encuentra el licopeno, que también puede ser encontrado en toronjas rojas, sandías y pimientos rojos. El licopeno es el antioxidante más potente contra los radicales libres (Candelas et al., 2006). La capacidad del licopeno para actuar como un potente antioxidante es responsable de proteger las células contra el daño oxidativo así, se reduce el riesgo de enfermedades crónicas (Rao et al., 1998). La influencia del enriquecimiento con CO 2 en el crecimiento del fruto, firmeza y color tiene efecto sobre las concentraciones de ácido ascórbico, ácidos orgánicos en las diferentes etapas de madurez en los frutos de tomate.
... SHT-11 single-chip integrated sensor is developed by Sensirion. It has two-line digital output with fullscale calibration and it can simultaneously measure moisture, temperature and dew point (Soto-Zarazua et al., 2011). It doesn't need peripheral components and it directly outputs the digital signal of relative moisture, temperature and dew point after the digital signal is calibrated. ...
In this study, a set of intelligent management system for fruits and vegetables greenhouses adapted to subtropical environment was set up based on wireless sensor network. The study results showed that this system could realize real time intervention on greenhouse temperature, humidity and illumination according to the database the sensor network collected. The development of his system realized implementation of refined cultivation of vegetables and fruits and could provide safeguard to realize ecological agriculture.
... In relation to temperature control, fish production in greenhouses has been practiced to inhibit extreme temperature changes in the water tanks. In the previous decade, various scientific studies of aquaculture have been performed in facilities that use greenhouses for temperature control (Snell et al., 2002; Soto-Zarazúa et al., 2010b), which mitigates the high economic cost of temperature control by mechanical heating systems that require energy sources, such as gas or electricity (known as forced methods) (Soto-Zarazúa., 2011). The natural water heating method, using greenhouses and solar radiation, requires only an initial investment for building the greenhouse structure and plastic cover, is economically viable (Omer, 2009), and has been proven effective in a great variety of applications (Medugu and Ndatuwong, 2009; Aliyu and Jibril, 2009). ...
This study was performed to evaluate the effect of tank position on the water temperature of fish culture tanks in aquaculture facilities inside a greenhouse. The temperature was measured in four 20 m 3 capacity tanks. Three points were measured for each tank: point A, the water in the fish culture tanks; point B, the environment outside of the tanks and point C, a point 0.3 m below the soil level. The measurements were recorded in periods of five minutes with data loggers. Significant differences in water temperature were found among the fish culture tanks with a grade of significance of 0.05 and environmental and soil temperature proved to exert an important influence on the water of the fish culture tanks. The mean value of the r-Pearson correlation found is 0.87.
... This control was simple, inexpensive and confident for a great variety of control applications (Soto-Zarazúa et al., 2010). The control circuit was connected to a relay (110 V, 60 Hz, 15 amp. ...
A thermosolar nursery development for rural producers was built and tested in three different operational conditions. The system consisted of a solar collector connected to a coiled heat exchanger inside a fish culture tank. A pump activated by two thermostats was used to circulate water throughout the heating circuit. A short-term pilot test was performed to probe the maximum heating of the system. Two more tests (Tests A and B) were carried out to evaluate the effect of heat insulation covering on the thermosolar fish tank. A student´s t-test was performed to assess the statistical difference in temperature and fish growth between the thermosolar tank and the control tank (which lacked thermal heating). The maximum temperature achieved by the system was 37.94°C. In Test A, the mean temperature of the thermosolar and control tanks after 49 days were 27.50 and 25.02°C, respectively. In Test B, the mean temperature after 38 days was 25.84 and 20.06°C, respectively. Differences in the weight and length of the fish were found. On test B, daily growth rate, survival and total biomass were higher in the thermosolar system than in control. The results suggest that the system can improve the growth performance and survival of the fingerlings.
... According to the Food and Agriculture Organization of the United Nations (FAO), advantages of greenhouses include the following: protection against extreme climatic conditions; controlled heating, cooling, shading and CO 2 enrichment; out-of-season harvests; improvements in crop quality; ground structure preservation; ability to sow selected materials; considerable production increases; reduced production costs; more efficient use of the growing area and lowered use of pesticides. Similarly, new, automated technologies are being promoted to help the grower in greenhouse operations (Soto-Zarazúa et al., 2010). ...
Tomatoes are in high demand because the world population consumes them daily. This research aims to improve tomato production and fruit quality through fruit measurement methods, which have a low impact factor on the fruit and plant during measurements. In the present paper, we present a review of the main attributes, such as color, ripening rate, firmness, shape, size and composition, that determine tomato fruit quality for final consumers; we also overview the methods (invasive destructive and invasive nondestructive) currently used to evaluate these attributes. The future trend in attribute analysis involves the development of portable, low-cost devices that take images directly from crops in the field to instantly determine quality characteristics.
... This separation between the crop environment and the external environment protects the crop from strong winds, acid rain or pests and allows control over the main environmental factors affecting plant growth: temperature, humidity, CO 2 concentration, light intensity (Gelder et al., 2012). All these variables should be controlled and managed automatically according to proper time schedules (Soto-Zarazua et al., 2011;Kacira, 2012). Due to their spatial distribution, the greenhouses are systems with distributed parameters (He, 2012); (Trejo-Perea et al., 2009;Patil et al., 2008), but currently, for the sake of simplicity, they are considered as MIMO systems with lumped parameters characterized by high coupling between the two main control variables: temperature and humidity. ...
This paper develops a control system for greenhouse climate with PID controllers tuned based on genetic algorithms. The greenhouse climate nonlinear model with dead-time and measurable disturbances is decoupled by feedback-feedforward linearization method, tacking the feedback without delay from an internal model. The equivalent system consists in two integrator plus dead-time channels for temperature and humidity suitable for PID control. The genetic algorithms (GA) with fitting objective cost functions are developed for PID tuning, including in initial population the PID controllers from four conventional tuning methods. Simulation results in real scenarios show improved performances for PID tuning by GA in comparison with conventional methods, where Ziegler-Nichols method is the best.
Greenhouses have been used to increase agricultural production. With the development of technology, they can now be automated. Many studies have been done on the automatic control of their microclimate, from intelligent control systems to Computational Fluid Dynamics (CFD) analyses, with the main purpose of optimal control of the microclimate and at the same time saving energy. This research concerns the process of modeling, design, and simulation of an automatic control system in greenhouses. More specifically, a virtual greenhouse (digital twin) is designed, and in it, the natural phenomena that take place in a real greenhouse are simulated. The program used for the simulations is Ansys FLUENT, suitable for CFD analyses. A branch of artificial intelligence, fuzzy logic, which is a method of replicating human thinking was utilized. To find the optimal control system, four fuzzy controllers were tested, and the optimal control system that the simulations indicated was implemented on an Arduino board using the LabVIEW program. The control was done at the temperature inside the greenhouse, with real weather data from a real greenhouse.
Technological interventions can play a significant role to keep optimal growing conditions in greenhouse industries with varying seasons and improve environmental performance by maintaining heating, cooling, and humidity levels. The research reported in this paper aims to integrate an Automated Climate Monitoring System (ACMS) for greenhouse in Ethiopia. Green- houses in Ethiopia require constant monitoring to properly maintain optimal climate conditions to grow plants all year round. To this end, they apply environment monitoring systems like timing devices, ON/OFF controllers and manual (human) monitoring which are prone to error. We propose a flexible ACMS prototype which can be applied without a user having the knowledge of how the system is made. The prototype system uses Advantech PC- cards and maintains the optimal climate condition (temperature, humidity and light intensity) of the greenhouses. Moreover, the easy to use graphical user interface provided by the system enables to combine the hardware and software functionalities for optimal manipulation of the environmental conditions. The system is centralized in that anyone can control several de- vices spread in the environment being monitored from one Computer. The performance of the system is found to be 88.89% which is “accurate and acceptable” result according to our confusion matrix. This indicates that better environmental supervision can be achieved when using the system. Moreover, the mean user acceptance (by the flower growers) of the ACMS and timing devices, ON/OFF implementations is 84.6% and 73.2%, respectively, highlighting the potential for integrating the ACMS into the greenhouse industry.
This paper presents a novel hierarchical control approach and new mathematical optimization models of greenhouses, which can be readily incorporated into energy hub management systems (EHMSs) in the context of smart grids to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control systems consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is proposed, so that it can be implemented as a supervisory control in existing greenhouse control systems. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Therefore, the proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied through Monte Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.
Purpose
– The purpose of this paper is to present a fuzzy logic-based control system for controlling the protected cultivation and describes its advantages over the traditional greenhouse automation control systems.
Design/methodology/approach
– In terms of systems theory, the greenhouse represents a complex non-linear system with emphasized subsystem interactions. Applying a non-linear control encompasses a number of difficulties due to incomplete knowledge of system dynamic. System decoupling is used in order to obtain simplified control structures for independent control loops. This gives limited results due to strong interaction between system variables and such control system does not allow optimization of system behavior primarily in terms of energy efficiency and/or water consumption.
Findings
– The paper presents a design of fuzzy logic-based controller, which optimizes the greenhouse energy and water consumption. The design includes the main linguistic variables for sensor and actuator subsystems. Membership functions of Fuzzy Inference System (FIS) are generated and simulation and analysis of the behavior of the designed control system is performed.
Research limitations/implications
– Obtained result shows that the designed control system beside its relative simplicity is flexible and adaptive, taking into account the differences in crop varieties and growth stages.
Practical implications
– Preliminary simulation of energy savings compared with the costs on actual field shows also good results. Still, number of different control strategies has to be applied in order to increase system flexibility regarding the different varieties and different stages of their growth.
Originality/value
– Obtaining an integrated controller based on fuzzy logic will highly improve possibilities for mass production of cheap technology. It will be easy to use by growers enabling them to incorporate their own informal growing knowledge and to create actual growing control strategy.
The pervasive merit of wireless sensor networks has led to a swelling interest in rendering them to be smart and autonomous which have the potential to enable a large class of applications in multiple fields. A novel cognitive wireless sensor-actor network based Greenhouse climate control (CWSAN-GH) is presented in this research. Fusion of artificial intelligent with nowadays Wireless Sensor Actor Networks (WSAN) is a promising era of smart WSAN. Primarily two most important greenhouse climate parameters are considered which are the temperature and humidity during diurnal and nocturnal time. Actuator and sensor nodes has been managed by CWSAN-GH Coordinator node (MGHSN) which mimics the brain of the planter to provide reliable, power conserving, autonomous control system of a greenhouse. The conventional control methods are not efficient in terms of energy, labor interference, productivity and flexibility. In this study, the adoption of Artificial Intelligent (AI) approach to controlling tasks within Wireless Sensor-Actor Network (WSAN) is presented. Fuzzy inference system has been designed and fused within the coordinator node of WSAN, hardware and software of network nodes and sensors has been presented. Initial field test for the CWSAN based greenhouse are also presented. The combination of AI with actor WSN proves high efficiently, cost effective method, beside flexibility of tuning the whole system for other agricultural tasks.
Evapotranspiration (ET) is one of the major components of hydrologic cycle. Accurate estimation of this parameter is essential for studies such as water balance, irrigation system design and management, and water resources management. Generally we used climate data for calculating evapotranspiration from indirect methods. This study investigates the utility of artificial neural networks (ANNs) for estimation of daily grass reference crop evapotranspiration (ET0) and compares the performance of ANNs with the conventional methods (Penman, Penman-Monteith, Stanghellini and Fynn) used to estimate ETo in Greenhouse. In the present study, the meteorological variables including air temperature, solar radiation, wind speed and relative humidity were considered daily. The daily outputs from on four physical ET and artificial neural networks have been tested against reference evapotranspiration data computed by the lysimeter to assess the accuracy of each method in estimating grass reference evapotranspiration in greenhouse. The accuracy of ANNs is the best but the accuracy of the Penman equation is the worse for estimating daily evapotranspiration compared with the other equations. In ranking the equations, Stanghellini equation, Penman-Monteith and Fynn, equation ranked in a second, third and forth places, respectively. The results showed the ANNs, Penman and P-M models overestimated ET, while the Fynn and Stanghellini models underestimated ET. The efficiency values of Penman, Fynn, P-M, Stanghellini and ANNs were 0.68, 0.72, 0.86, 0.907 and 0.93 respectively.
This work analyzes an energy consumption predictor for greenhouses using a multi-layer perceptron (MLP) artificial neural network (ANN) trained by means of the Levenbergh-Marquardt back propagation algorithm. The predictor uses cascade architecture, where the outputs of a temperature and relative humidity model are used as inputs for the predictor, in addition to time and energy consumption. The performance of the predictor was evaluated using real data obtained from a greenhouse located at the Queretaro State University, Mexico. This study shows the advantages of the ANN with a focus through analysis of variance (ANOVA). Energy consumption values estimated with an ANN were compared with regression-estimated and actual values using ANOVA and mean comparison procedures. Results show that the selected ANN model gave a better estimation of energy consumption with a 95% significant level. The study resents an algorithm based in ANOVA procedures and ANN models to predict energy consumption in greenhouses.
Although natural ventilation plays an important role in the affecting greenhouse climate, as defined by temperature, humidity and CO2 concentration, particularly in Mediterranean countries, little information and data are presently available on full-scale greenhouse ventilation mechanisms. In this paper, we present a new method for selecting the parameters based on a particle swarm optimization (PSO) algorithm which optimize the choice of parameters by minimizing a cost function. The simulator was based on a published model with some minor modifications as we were interested in the parameter of ventilation. The function is defined by a reduced model that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamic behavior of the inside air temperature and humidity during ventilation. Our approach is validated by comparison with some experimental results. Various experimental techniques were used to make full-scale measurements of the air exchange rate in a 400 m2 plastic greenhouse. The model which we propose based on natural ventilation parameters optimized by a particle swarm optimization was compared with the measurements results. Copyright © 2009 IFSA.
In high-fish-density aquaculture systems, tilapia producers are compelled to provide 100% of food required to obtain profitable
growth rates. It is well known that fish have a low food conversion rate and feeding represents the most important expenditure,
approximately 40% of total production cost. Therefore, precise quantities of food should be provided to avoid water pollution
and economic losses due to food waste when water conditions are inadequate for fish feeding. A way to control food provisions
in this work was determined by the conditions of temperature, dissolved oxygen, fish age, and body weight, since these variables
have a direct effect on fish metabolism and growth. Thus, a change in metabolism is reflected in a modification of energy
requirements and, as a consequence, in variations of food consumption. In this work, a new feeder with fuzzy-logic control
algorithms is proposed for fish feeding; this technique allows farmer knowledge to be taken into account in a series of if–then-type
rules. To define these rules the temperature and dissolve oxygen were considered in order to provide precise food quantities.
The results show minimal differences in growth (P>0.05) between treatments, important food saving of 29.12% (equivalent to 105.3kg), and lower water pollution (reduced
water dissolved solids and ammonium components) compared with timed feeders. This system provides an important contribution
to sustainability of intensive aquaculture systems, increasing productivity and profitability, and optimizing water use.
KeywordsTilapia-Temperature-Dissolved oxygen-Feeding-Fuzzy-logic control-Intensive aquaculture systems
Operational meteorology is perceived as a fuzzy environment in which information is vaguely defined. The mesoscale processes
such as fog, stratus and convection are generally dependent on the topography of the place and has always been difficult to
forecast for the meteorologists. The main objective of the present study is to introduce the concept of fuzzy inference system (FIS) in the prediction of fog. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base
consists of a collection of fuzzy IF-THEN rules. The fuzzy inference engine uses these fuzzy IF-THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse
based on fuzzy logic principles. Basic weather elements, which affect weather characteristics of fog, are fuzzified. These
are then used in fuzzy weather prediction models based on fuzzy inferences. These models are simulated and the crisp results
obtained using developed defuzzification strategies are compared with the actual weather data. The basis of methodology is
to construct the fuzzy rule base domain from the available daily current weather observations in winter season over New Delhi.
The results reveal that dew point spread and rate of change of dew point spread are the most important parameters for the
formation of fog. The results further indicate that fog formation over New Delhi are dominant when (i) dew point is greater
then 7°C along with dew point spread between 1 and 3°C. (ii) rate of change of dew point spread must be negative and wind
speed should be less than 4 knots. This study presents a technique for predicting the probability of fog over New Delhi for
5–6 hours in advance. The skill score indicates that the performance of FIS is appreciably good. The method is found to be
promising for operational application.
This paper presents a comparison of different evolutionary algorithms (EAs), such as Genetic Algorithms (GAs), Evolutionary Strategies (ES) and Evolutionary Programming (EP) to calibrate parameters of a climate model that describes the behaviour of air temperature and relative humidity (RH) within a greenhouse where a tomato crop is being grown. The objective was to determine which method generates parameter values that give the best prediction of the environment of a greenhouse located in the central region of México. Simulation and analysis of the climate model show that the estimations of the inside temperature and RH are closest to the measurements when EP was used to calibrate the parameters of the greenhouse model.
Corporations interact with society and the physical and biological environment in ways that affect both sides. In this capacity,
corporations play an important role in the sustainability of a region or country. Symmetrically, a corporation’s sustainability
depends on the sustainability of its wider environment. In this paper, a multi-stage fuzzy reasoning model is presented to
assess a corporation’s sustainability. The model has two fundamental components: human and ecological. The human component
has up to four inputs: economic, political, knowledge, and welfare. The ecological component can also have up to four inputs:
air, water, land, and biodiversity. Each of these eight components has a number of basic inputs suitable for evaluating a
given corporation. The model can be used to assess a corporation’s sustainability, record its historical evolution, and compare
the company to its direct competitors. Equally importantly, sensitivity analysis of the model reveals the most important basic
indicators affecting corporate sustainability, identifying areas which managers and executives should place special attention.
An application example based on three large international corporations in the same industry illustrates the usefulness of
the model.
The aim of study was to investigate the effect of temperature on air patterns in two different greenhouse configuration using two-dimensional computational fluid dynamic models. The models were constructed on the basis of two major assumptions: first, the inside temperature is considered to have a vertical distribution while it is constant on horizontal planes; second, temperature is the main driven force that causes air movement. The computational results were validated with current literature data. The models were later used to study the air patterns inside two greenhouse configurations under zero and low wind velocities. The ventilation efficiency was assessed on the basis of the ventilation rate and complemented with the study of the internal air velocities at a height of 1.5 m. Results indicate that applying temperatures as the main driven forces for the buoyancy effect produced a positive correlation and agree with current literature data, therefore, providing a simple way to study the ventilation and inner air patterns. Results also, show that the ventilation in greenhouses due to the temperature effect produces high air exchange rates. However, the study of the inner air patterns reveals that those air patterns occur near the openings causing almost no air exchange in the greenhouse central area due to a stagnant effect that reduces the wind effect throughout the greenhouse.
In this study, we are interested in regulating two important variables inside of the greenhouse: temperature and CO2 enrichment for two cabins in a experimental greenhouse at the Humboldt University of Berlin. Predicting the behavior of these two variables and photosynthesis will allow us to turn on and off the controls such as heating system, vents opening or CO2 enrichment at the right time, in order to save energy and keep the plants inside of the comfort zone. Artificial Neural Networks (ANN) were used because of their ability to capture the non linear relationships governing the changes in the greenhouse environment. Temperature was predicted 5 and 10 min ahead of the sensor signal, with MSE errors between measured and predicted values of 0.088 and 0.029, respectively. The CO2 predicted from the model was used as an input in the photosynthesis model. In this last model, seven variables were used and the predictions were highly precise with a MSE errors of 0.0563 and 0.0974 for photosynthesis 5 and 10 min ahead, respectively. A sensitivity analysis was performed in the photosynthesis model showing that relative humidity is an important variable for CO2 levels and for the photosynthesis process. The predicting models will allow to achieve our final goal which is to replace the sensors and give predictive information for a higher control quality in an open loop control system.
A new adaptive technique is proposed for the control of the temperature in a greenhouse whose parameters vary with operating conditions. The proposed adaptive controllers are derived by solving either the pole-placement or the linear quadratic regulation (LQR) problem and use multirate controllers in which the greenhouse temperature is sampled many times over a fundamental sampling period. On the basis of the proposed control scheme, the problem is reduced to an associated discrete-time problem for which constant state feedback controllers are derived. Thus, the proposed technique essentially becomes one of computing a constant gain controller rather than one of deriving state observers or output feedback controllers, as in other indirect adaptive control techniques. As a consequence, the exogenous dynamics introduced in the control loop is of low order. Furthermore, the proposed adaptive controllers can be designed to possess any degree of stability, since their transition matrices can be chosen arbitrarily. The proposed scheme estimates the unknown parameters of the greenhouse on-line from sequential data on the greenhouse temperature and the heating power, which are recursively updated. Persistency of excitation of the controlled system is assured without making assumptions on the system under control, other than controllability, observability and known order. Simulation results of the greenhouse dynamics illustrate the effectiveness of the proposed scheme.
The use of evolutionary algorithms for calculation of the optimal control of the states of a greenhouse system will be presented. The integrated model employed (green-house climate, crop growth, outside weather conditions and control equipment) predicts temperature, air humidity and CO 2 concentration in a time interval of 15-60 minutes (short time-scale model). The paper presents the optimization of the control of the greenhouse climate to maximize the profit under certain constraints (for instance, prevention of stress for the crops) using evolutionary algorithms. By incorporation of problem specific knowl-edge into the evolutionary algorithm better results were produced in a shorter time. The re-sults of optimization for optimal control using real world weather data are shown.
This paper proposes a contribution on the application of fuzzy logic to the identification and the command of the multi-dimensional systems. We present several techniques of fuzzy logic to identify and to command the Multi Input-Multi Output (MIMO) process. The researched models have a black box structure and the rules are determined by numerical approaches (no the expert inter-vention). We describe a method to reduce the complexity of a fuzzy controller and we show an application on a real system (a greenhouse).
A review is presented of several potentially useful applications of artificial neural networks (NN) to greenhouse climate control. Subjects covered are: Quasi-steady-state modelling, reduction (compression) of input and state vectors, NNs used as difference equations and replacing controllers (algorithms or humans) with NNs. In this context the strength of NNs is their flexibility to adapt to non-linear and non-physical data. Their main disadvantage is that their proper training requires large multi-dimensional sets of data to reduce the risk of extrapolation. Therefore, minimizing the dimensionality of the problem (both input and state vectors) becomes of paramount importance. Bottleneck NNs may be used for this purpose.
The goal of this expository paper is to bring forth the basic current elements of soft computing (fuzzy logic, neural networks, genetic algorithms and genetic programming) and the current applications in intelligent control. Fuzzy sets and fuzzy logic and their applications to control systems have been documented. Other elements of soft computing, such as neural networks and genetic algorithms, are also treated for the novice reader. Each topic will have a number of relevant references of as many key contributors as possible.
Fuzzy modeling and control based on the fuzzy sets theory have been used in the biotechnology field for the last two decades. Recent studies on fuzzy modeling and control of various biological processes are reviewed. In addition, five applications of fuzzy control to industrial biological processes are summarized, compared and discussed in terms of the system features, control purpose, input and output variables, development of fuzzy rules and effectiveness. Fuzzy modeling and control are regarded as promising methods for automating the bioprocesses in which experienced operators play significant roles in their successful operation.