An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Control and Assessment

Transcription

1 Original Article J. of Biosystems Eng. 42(1): ( ) Journal of Biosystems Engineering eissn : pissn : An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Roop Pahuja*, Harish Kumar Verma, Moin Uddin *Department of Instrumentation and Control Engineering, National Institute of Technology, Jalandhar, Jalandhar , Punjab India Received: October 22, 2016; Revised: May 16, 2016; Accepted: June 4, 2016 Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance. Keywords: Adaptive and selective fuzzy controller, Collaborating data processing, Greenhouse microenvironment monitoring, statistical analysis, VPD-based greenhouse climate control, Wireless sensor and actuator network Introduction The wireless sensor network (WSN) is one of the upcoming, multidisciplinary, and pervasive computing technologies of the 21st century (Estrin et al., 2002). With its wide range of application potential, it is invading environments and bridging the gap between the real physical world of sensory data and the digital world of computers and internet. WSN is achieving the high-end goal of creating real sensor webs, which may be called the next-generation internet (Zhao and Guibas, 2004). A *Corresponding author: Roop Pahuja pahujar@nitj.ac.in typical WSN consists of a number of sensor nodes, spatially distributed into an application environment, that cooperatively monitor the phenomenon of interest and communicate data to the gateway node via a low-power true-mesh multihop routing for end user access (Sohraby et al., 2007). Each sensor node is embedded with sensing, computing, and wireless communication capabilities to form a data collection network. The utility of WSN is further enhanced by integrating it with an actuator network (wireless or wired) or by incorporating elements of control and actuation in the wireless nodes to form what is known as a wireless sensor and actuator network (WSAN) (Pahuja et al., 2013). With the continuous advancements in microelectronics, Copyright c 2017 by The Korean Society for Agricultural Machinery This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

2 micro-sensors and micro-electromechanical systems (MEMS), data acquisition systems, embedded systems, and wireless communication networks, research in WSN has matured to the extent that application-specific WSNs are gaining pace. The IEEE based ZigBee (ZigBee Alliance, 2004) and a ZigBee-like propriety networking protocol, XMesh (Crossbow, 2007a), among others, ported to some of the node hardware platforms, have made significant impact on a broad range of applications. Over a few years, many applications of WSN have appeared in areas such as building automation (ZigBee Alliance, 2006), (Kazmi et al., 2014), health care (ZigBee Alliance, 2008), defense and surveillance (He et al., 2006), (Sun et al., 2011), precision agriculture (Burrell et al., 2004), (Tim et al., 2007), (Keshtgari and Deljoo, 2012), (Wu et al., 2015), disaster management (Ahmad et al., 2011), transportation systems (Katiyar et al., 2011), (Felemban and Sheikh, 2014), and others (Li and Xiong, 2013), (Mehdipour et al., 2013). With its inherent advantages of wireless, flexible installation, and reliability of operation, WSN has also gained popularity in the intensive area of controlled-environment agriculture (CEA) technology, especially greenhouse (GH) horticulture. Yiming et al. (2007) discussed the hardware and software design of a ZigBee WSN node with temperature, relative humidity, and moisture sensors and proposed a star network topology inside the greenhouse and a mesh network architecture for the connection between the greenhouses and the management system. Liu et al. (2007) discussed a WSN prototype with a two-part framework for greenhouses. The first part discussed the use of Crossbow nodes for measurement of temperature, light intensity, and soil moisture. The second part consisted of designing the management software for logging and displaying data, along with the experimental testing of the antenna height that affected the radio range. Moreover, it measured the temperature, relative humidity, and lighting conditions above the crop. Ahonen et al. (2008) reported the development of a wireless sensor network using a Sensinode sensor platform fitted with temperature, relative humidity, and light intensity sensors, based on a 6LoWPAN protocol. The authors conducted a one-day experiment in a greenhouse in Western Finland. The data were used to evaluate the network reliability and its ability to detect microclimate layers. Hwang et al. (2010) reported the establishment of a ubiquitous paprika greenhouse management system using WSN technology. The proposed system collected and monitored information related to the growth environment of crops outside and inside paprika greenhouses by installing WSN sensors and monitoring images captured by the CCTV cameras. It also provided an environment control facility for manual and automatic long-distance control and facilitated an optimized environment to grow paprika. Park and Park (2011) reported on a WSN-based greenhouse environmental monitoring and dew point control system that prevented dew condensation phenomena on the crop surface and played an important role in the prevention of disease infections. Berezowski (2012) reported the landscape of short-range wireless communication technologies for greenhouse management. The author, based on application domain knowledge, identified the design constraints and presented possible solutions for applying wireless networks to greenhouse WSN. The purpose of this paper was to make computer engineers aware of this specific application domain and the space it offered for applying communication infrastructure; additionally, to make horticulture researchers aware of what wireless technologies would be required and how to optimize their usage in greenhouses. Hussain et al. (2013) reported on the design and implementation of an XBee based wireless sensor network for greenhouse control using star topology. The authors discussed the hardware components of the XBee sensor node and the node configuration and its interfacing to low cost analog sensors for monitoring the essential network parameters such as temperature, humidity, and light intensity. Moreover, a system driving software in Visual Studio.net was designed with a rule-based controller to vary the state of actuating devices. Background Greenhouse is a mini-factory that produces crops or crop end products under controlled environmental conditions (Hanan, 1998), (Kamp and Timmerman, 2003). During the past twenty years, the progress in greenhouse engineering and design has allowed impressive yields in controlled environment agriculture. Even today, many issues in the greenhouse automation process require attention to improve greenhouse cropping. A novel preliminary system, based upon a recent wireless sensor network technology, has been implemented by the authors (Pahuja et al., 2013). The purpose of this system is to tackle problems that have not yet been addressed and that to a large 24

3 extent are related to the knowledge of the greenhouse microenvironment, to automate the plant related greenhouse operations and decisions, and to provide online information access to vital greenhouse environment control parameters. This system is further upgraded with new functions and features and a few changes in the user interfaces. It has been implemented as the integrated GHAN system that is discussed here. Moreover, the field performance of the system has been tested under different climatic conditions and an in-depth analysis of microenvironment monitoring and control operation has been done with the newly developed assessment tool, in order to project its utility during online operation and post analysis of the greenhouse microclimate data. Contribution This paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access the greenhouse microenvironment. GHAN, which is an upgraded system, has many new functions with improved technicality and user interaction. It is an intelligent WSAN (Wireless Sensor and Actuator Network) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation system and helps the grower with valuable information not available otherwise. Important contributions are summarized as follows: (a) Apart from providing online spatial and temporal monitoring of greenhouse microclimates, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better and faster control of the greenhouse VPD with energy optimization. The controller is reconfigurable to cope with practical situations of greenhouse. Moreover, greenhouse energy consumption data are available online, as an important utility parameter. (b) In addition to climate control, the GHAN system has an irrigation management module. Using the latest soilmatrix potential sensors, the system monitors when, where, and how much to irrigate and manages the irrigation schedule spatially, within greenhouse grids. (C) The power of the GHAN system lies in extending its configuration capabilities to customize not only the network deployment and the limits of control variables, but also controller parameters such as the selection of a humidifying and dehumidifying controller mode, type, number and power rating of climate control equipment, auto/manual control operation and selection of soilmoisture sensors. Furthermore, web connectivity is enabled for remote monitoring of GHAN over internet. (d) Further, as a need to understand the microclimate control dynamics of greenhouse during the crop season or during a specific time, a greenhouse microclimate assessment tool is proposed and implemented. Based upon greenhouse-recorded data using GHAN system, the assessment tool collaboratively and statistically analyses the overall optimality and spatially variability of the greenhouse in terms of climate control index and spatial standard deviation. The quantitative analysis of historical climate trends helps the greenhouse manager to take better decisions in the future. (e) The system was also field-tested in a commercial greenhouse. Daily results of the greenhouse microclimate control dynamics were recorded and analyzed to evaluate the system behavior and performance. For most of the time, the climate control index value remained high ( ) for the appropriately humidified (90% of time) greenhouse, when the outside VPD was high, thus providing a conducive climate for the plants to grow in healthy condition. The following sections briefly explain the system architecture, presenting the hardware and software components, the online system operation on GUI, the field performance of the system under different weather conditions, conclusion, and future scope. Materials and Methods WSAN is the most recent and preferred sensing and control technology for the next-generation greenhouses. It provides advantages of low cost and ease of installation, flexible deployment, and in-situ distributive multimodal sensing architecture that is well suited for spatial monitoring and control of the greenhouse environment. An intelligent virtual-instrumentation based WSAN system, named GHAN, has been redesigned and developed to provide many more services for site-specific monitoring and the assessment of greenhouse important climate variables and irrigation management. Figure 1 shows a typical 3-tier architecture layout of the GHAN system that consists of 25

4 Figure 1. A typical 3-tier architecture of the GHAN system. the greenhouse network, server, and remote user connectivity tiers. The sections below explain the most important components of the system. Greenhouse network tier The greenhouse network tier performs the operations of sensing and control. It has two components: the ZigBee enabled greenhouse wireless sensor network (GH-WSN) and the RS-485 wired actuator network. (a) Greenhouse wireless sensor network: the upgraded GH-WSN measures the soil-moisture at specific grids, along with climate variables. GH-WSN consists of a few sensor nodes deployed at specific locations within the greenhouse to measure environmental variables, an outside node to measure weather parameters, and a gateway node connected to the server in a greenhouse control room to facilitate data communication and collection. The node hardware consist of an IRIS mote and MDA300 sensor board (Crossbow, 2007b, 200c) with an on-board digital temperature and relative humidity sensor (Sensirion, 2007) to measure temperature and relative humidity at specific locations. To support greenhouse irrigation management, soil matric potential sensors (Irrometer, 2005a) with proper signal conditioning units are integrated with the canopy sensor nodes. The soil matric potential sensor output voltage in the range V is pre-calibrated (Spaans, 1992) to measure the relative indication of saturated to very dry state of the soil, in terms of soil matrix potential varying in the range of 1-70 kpa. The nodes within the greenhouse are deployed using a grid-height deployment scheme (Pahuja et al., 2013). In this scheme, sensor nodes are deployed spatially within uniformly distributed grids at the aerial and canopy height levels to measure greenhouse climate variables such as aerial temperature, aerial relative humidity, canopy temperature, and canopy relative humidity along with outside weather parameters. The nodes within the greenhouse are hand-placed at the designated locations and are static in nature. For deploying sensor nodes within the greenhouse, as shown in Figure 2, the greenhouse floor area is virtually partitioned into a number of grids of uniform size (20 30 m 2 ), with two height levels, designated as canopy height (h1) and aerial height (h2). At each grid and height level, at least one sensor node, preprogrammed with the relative location node ID, is hand-placed to measure the local variation in the environmental variables. Canopy height denotes the height where the canopy nodes are placed to measure temperature and relative humidity, near the leaves of the crop. As the crop grows, the height level varies from ground level to cm within the canopy. Aerial height denotes the height where the aerial nodes are placed to measure temperature and relative humidity of the greenhouse air above the crop. It varies from 150 to 200 cm above the canopy root zone. Each location is identified with its location identification label g i h j, where i denotes the grid number (1 to n, where n is the maximum number of grids in the greenhouse) and j denotes the height level (1 or 2). There is a tradeoff between the parameters, such as the selection of grid size, number of nodes, or cost, and spatial resolution with which the greenhouse climate is monitored. The larger the number of grids, the better is the spatial resolution and higher is the network cost, and vice versa. If the grid size is increased beyond a certain limit (the radio range of the node, e.g., 50 m), this can sometimes lead to loss of information Figure 2. Grid-height based deployment scheme for greenhouse wireless sensor network. 26

5 and network connectivity. In contrast, a too small grid size can lead to the additional cost of placing more nodes without much gain in the measurement of the spatial variability of climate data. As a rule of thumb, it is recommended to have at least three grids along the width of the greenhouse covering the greenhouse area, one at the center and other two grids near the side walls, to extract microclimate variations (Timmerman, 2003). A grid size in the range of 200 to 500 m 2 is suited to characterize the spatial variation of the greenhouse environmental variables, keeping in mind that adjacent nodes should be within the indoor radio range (typically 50 m) (Crossbow, 2007 b) of each other. This helps the nodes to relay data effectively using multihop routing. Before deployment of sensor nodes at designated locations in the greenhouse, each sensor node is pre-programmed with its location based ID and an embedded TinyOS (TOS) (Levis, 2006) based application program. GH-WSN is a robust, true-mesh, multihop network based upon the IEEE and XMesh routing protocol, operating in 2.4GHz ISM band (Crossbow 2007a). The nodes are programmed to periodically transmit the average sensor data wirelessly, within a suitable time frame of min, to the gateway node using the underlying ZigBee-like XMesh routing protocol for further processing and display on the host PC/server. The nodes operate in its low duty cycle, thus sustaining a field life of approximately a month, over a pair of AA cells with nominal voltage and battery capacity of 3.0 V and 2000 mah, respectively. (b) Greenhouse actuator network: This network interfaces the controller signals generated by the server/host PC to the climate control equipment housed in the greenhouse, using an established RS-485 industrial serial networking standard. The network supports high level of noise immunity, long distance data transmission, high-speed communication, easy connection and extension of network devices using minimum wiring requirements (Advantech, 2008). The RS-485 serial networking standard allows multiple actuator nodes (field devices or slaves) to be connected to the network communication module (master) over differential signal lines (bus) of RS-485 in multi-drop manner, to form a master-slave network. In this network, the master, connected to PC, communicates with the slave modules using a command-response protocol in bidirectional and half-duplex mode. In this case, the slave modules are distributed digital-output control modules. Digital-output channel (lines) are interfaced to actuating elements (DC relays) to drive different climate control equipment components at a variable rate. In response to the climate controller output, the RS-485 actuator network driver program issues control commands (Advantech, 2008) to the digital ports of the module to activate respective relays to drive the end devices of the climate control equipment (roof vents, shade screen, exhaust fan, cooling pad, heaters) with variable load. Server tier The server tier provides connectivity of the greenhouse network tier to the server/host PC. It runs a greenhousededicated application software named GEMCS (greenhouse microenvironment monitoring and control software), an advanced version of software as discussed by the authors (Pahuja et al., 2013). The software controls and synchronizes the operation of the greenhouse XMesh-WSN and the RS-485 actuator network for online monitoring and control of greenhouse microclimate. It also provides irrigation management and has several additional intelligent features. Programmed for discrete, point-by-point packet acquisition and time series analysis (Robert and David, 2000) of network multivariate data at each instant of time, the software executes all functions and updates vital greenhouse microenvironment information on the GUI at a fast update rate with a time interval of 1 min. GEMCS has multiple functional modules with enhanced functionality, as shown in Figure 3. It not only supports the existing functions of online monitoring of the greenhouse microclimate, spatial and temporal analysis of GH-crop VPD (Pahuja et al., 2013), (Prenger and Ling, 2000), greenhouse VPD based crop-stress and crop-disease risk prediction, network connectivity and health status monitoring along with data logging, but also has many additional functions as explained below: (a) Integrated climate controller (ICC): It is an improved version of the existing climate controller. ICC regulates the GH-crop VPD within the desired limits, irrespective of the outside weather fluctuation, at a fast rate, along with saving of greenhouse energy consumption. Figure 4 shows the model of ICC with an inner loop MIMO (multi-input multi-output) fuzzy controller operating 27

6 Figure 3. Simplified functional block diagram of greenhouse application software GEMCS. Figure 4. Model of ICC (integrated climate controller) with inner loop MIMO (multi-input multi- output), fuzzy controllers operating in feedforward-feedback mode of operation, and the outer loop selective and adaptive control mechanism. in a feedforward-feedback mode of operation, and outer loop, and a selective and adaptive control algorithm (Bhanot, 2008), (Both, 2007). In response to the average GH-crop VPD, the outside VPD error and rate of change of the VPD error, multi-range fuzzy controllers programmed with expert control rules, the issue of multilevel signals to drive the different climate control equipment at variable discrete loads to regulate the average GH-crop VPD. The cooling pad operates in three states (0, 1, 2) that specify 0% (OFF), 50% (HALF) and 100% (FULL) load, respectively. The exhaust fan ventilation operating status varies in five states (0, 1, 2, 3, 4) that specify 0% (OFF), 25% (LOW), 50%(MEDIUM), 75% (HALF), and 100% (FULL) load respectively. The roof ventilation operating status varies in two states (0, 1) that specify 0% (CLOSE) and 100% (OPEN) conditions, respectively. The temperaturebased shade screen controller with dead band operates the shade screen in two states (0, 1), which specify 0% (CLOSE: UNCOVERED) and 100% (OPEN: COVERED) conditions, respectively. At any time instant, based upon the value of the input control variables and the selective and adaptive control mechanism, certain rules of the inner loop fuzzy controllers are activated or fired to provide the multilevel controller outputs. The control signals are simultaneously interfaced to the actuator network to drive different equipment at suitable loads. This appropriately humidifies (increase in VPD) or dehumidifies (decrease in VPD) the greenhouse at a fast rate, with saving in energy consumption, in response to different outside weather conditions. With respect to the previously designed controller (Pahuja et al., 2013), this modified controller has energy saving control rules and an adaptive mechanism to switch the climate control equipment to lower loads as the evening time approaches and the outside VPD falls at a fast rate. Moreover, when outside VPD is favorable, no climate control equipment is operated, except roof vents, resulting in very low energy consumption. This adaptive decrease in the operating load of the equipment components results in saving of energy. The energy saving is about 25% or higher just in one hour when the controller adaptively operates climate control equipment at lower loads. Furthermore, many of the controller parameters are configurable. The configuration panel (Figure 5 (a)) allows flexibility to the grower for selecting the suitable auto/manual control operation, and the operating mode (low error/low power) of the humidifying/dehumidifying fuzzy controller under certain conditions dictated by plants needs or economic constraints, and readjusts the controller parameters accordingly. The section below briefly explains the design of the inner loop fuzzy controller. The inner loop MIMO fuzzy controller consists of humidifying and dehumidifying fuzzy controllers to regulate the average GH-crop VPD. When the outside VPD error (e out ) and inside VPD error (e in ) are positive, greenhouse is appropriately humidified using suitable ventilation and cooling system fuzzy controllers known as humidifying fuzzy controllers (HC). The heating system remains OFF. The humidifying fuzzy controller consists of six dual input single output fuzzy controllers, each with the rule base of 7 7, to 28

7 drive ventilation and cooling system in three error ranges (low, medium and high). Depending upon the VPD error (difference between outside VPD and VPD high set limit), suitable (low/medium/high) the range of the humidifier controller is triggered. Based upon the present state of controller inputs and control rules, the controller produces an output to vary the operating load/status of roof vents, exhaust fans and cooling pad in discrete levels. The devices are operated in a manner that collectively vary the rate at which the greenhouse climate is humidified to reduce GH-crop VPD error. Each humidifying fuzzy controller operating in FF/FB mode has two inputs, the VPD error and the rate of change of the VPD error. These controller inputs are normalized within a suitable range and are fuzzified into seven linguistic terms (Extremely Small: ES, Very Small : VS, Small : S, Medium : M, High : H, Very High : VH, Extremely High : EH) and (Negative Large : NL, Negative Medium : NM, Negative Small : NS, Near Zero : NZ, Positive Small : PS, Positive Medium : PM, Positive Large : PL), respectively defined by a symmetrical triangular and trapezoidal membership function. The controller output for the ventilation controller is fuzzified into five linguistic terms (Very Low : VL, Low: L, Medium : M, High : H, Full : F), defined by singleton membership functions. The controller output for the cooling system is fuzzified into three linguistic terms (Off: O, Half: H, Full: F), defined by singleton membership functions. Each of the ventilation and cooling system controllers in the particular range has a unique set of If-Then control rules of 7 7 that combine the input conditions to provide output states. The rules are devised taking into account the interactive effect of different ventilation and cooling systems in controlling the GH-crop VPD. The ventilation controller output is mapped to drive roof vents and exhaust fans. Roof vents are operated in either of two states, 0 or 1, indicating close or open condition. The exhaust fan is operated in either of five operating states, Off: 0, Low: 1, Medium: 2, High: 3, Full: 4, indicating 0%, 25%, 50%, 75%, and 100% operating load, respectively. The cooling system controller output drives the cooling pad in either of three operating states, Off: 0, Half: 1, Full: 2, indicating 0%, 50%, and 100% operating load, respectively. When a positive VPD error is very low (<5 mb), the low range HC keeps the roof vents in OPEN state. As the VPD error increases (5 10 mb) and the rate of VPD error also increases, the exhaust fan operates at higher loads (25%, 50%, 75%, and 100%), i.e., switching a larger number of fans to the On state gradually. With a further increase in the VPD error (>10 mb), the medium and high range controller switches the cooling system to a higher status. The roof vents are switched OFF and the exhaust fan initially operates at a lower load. With further increase in the VPD error after the cooling system has been operated at maximum load, the exhaust fan switches to higher states. This causes loss of the heat that has accumulated at the other side, the farthest from the cooling pad of the greenhouse. As the VPD error and rate of VPD error decreases, the devices are switched to lower status/loads. The humidifying fuzzy controller is also adaptive to the user selective controller mode (low error 0/1, low power 0/1) of operation. The VPD range and triggering point of each of the humidifying controllers is readjusted and customized by the adaptive control mechanism. The controller operating mode controls the triggering point of the humidifying controller (HCTP) in terms of the minimum VPD error value required to trigger the cooling system. By default, the controller operates in the lowest error mode low error mode 0 with the lowest value of HCTP. In this case, the cooling system is triggered at the lower VPD error of 10 mb. Furthermore, an increase in error increases the exhaust fan ventilation and cooling system load to minimize GH-crop VPD error at the faster rate, but at the cost of high energy consumption. This condition is well suited during an extreme summer that demands fast humidification of the greenhouse to drive cooling pad or foggers. Shifting from low error to low power mode of HC increases the triggering point and range of HC. Devices are operated at lower loads. This reduces the overall energy consumption but increases the instantaneous error for short time duration. The dehumidifying fuzzy controllers are activated under conditions when the outside VPD error (e out ) and inside VPD error (e in ) are negative, (0 to -6 mb). In that case, the greenhouse is appropriately dehumidified using the heating system fuzzy controllers known as the dehumidifying controller (DC). Depending upon an user selectable DC mode (low error/low power), 29

8 the dehumidifying fuzzy controller parameters such as triggering point and range of dehumidifying fuzzy controller is readjusted and adapts itself to new changes in requirements. Controller inputs such as negative VPD error and rate of change of VPD error are normalized within the ranges and fuzzified into seven linguistic terms (Extremely Small: ES, Very Small : VS, Small : S, Medium : M, High : H, Very High : VH, Extremely High : EH) and (Negative Large : NL, Negative Medium : NM, Negative Small : NS, Near Zero : NZ, Positive Small : PS, Positive Medium : PM, Positive Large : PL) respectively defined by the symmetrical triangular and trapezoidal membership function. The controller output is fuzzified into five linguistic terms (Off: O, Low : L, Medium : M, High : H, Full :F) defined by singleton membership functions. The dehumidifying controller output is mapped to operate heaters at five operating states Off: 0, Low: 1, Medium: 2, High: 3, Full: 4 indicating 0%, 25%, 50%, 75%, and 100% operating load, respectively. The heating controller has a unique set of If-Then control rules that combine the input conditions to provide the controller output states. The rules are devised taking into consideration that as VPD error is becoming more negative and rate of change of error is becoming more negative, the heating system is switched to higher load. When the rate of change of the negative error is becoming more positive the heating system is switched to lower load. However, other devices such as roof rents, exhaust fans, and cooling pad remain at Off state. By default, the controller operates in low error mode and has a lower triggering point for the heating controller. In low power mode, the heater triggering point is increased and thereby the controller range and rules are readjusted. The same rules are activated at a higher VPD error, thereby decreasing the power load on the heaters with compromise on the average VPD error. At any time instant, depending upon the current values of the VPD error and change of VPD error, certain rules of the inner loop fuzzy controllers are fired to provide the multilevel discrete controller outputs. This drives all the climate control equipment components simultaneously at the respective loads to regulate the average GH-crop VPD. (b) Controller error and energy consumption monitor: This functional module evaluates some of the most important parameters related to the performance of the greenhouse climate controller. It calculates controller error characteristics and energy consumption of the greenhouse. At each instant of time, this functional module calculates the absolute and mean controller error and the error standard deviation. It also provides the maximum energy consumption curve of the greenhouse based upon the duty cycle of the different climate control equipment components under different loads and user fed information, related to the power rating and number of units of climate control equipment components housed inside the greenhouse. The energy consumption of greenhouse is one of the utility-based cost parameters concerning the grower that governs the overall operational cost of the greenhouse and affects the profit thereby. (c) Irrigation management: Providing sufficient amount of water to the crop at the right time is one of the key requirements in crop cultivation (Ayday, 2009). Instead of using a fixed time-based scheduling mechanism, the irrigation management module supports dynamic irrigation scheduling that depends upon outside weather, greenhouse climatic conditions, and crop-irrigation requirements during its growth cycle. This irrigation management module is implemented using a soil matric potential sensor based feedback mechanism to provide better decisions of water requirement (soilmoisture status) of the crop to maintain healthy root zone conditions. The soil-moisture sensors are integrated with canopy nodes to monitor soil moisture in terms of SMP (soil matric potential) in kpa (kilo Pascal) (Irrometer, 2007a). This module fuses the information from the soil matrix potential sensors and crop-stress risk prediction model to identify when, where, and how much time to irrigate in a greenhouse. It safe guards the greenhouse crop from the adverse effects of under-watering or over-watering and optimizes the use of the water resource. The raw soil-moisture voltage data gathered from the WSN are preprocessed using an event based detection algorithm and a sampled moving average method to provide the latest (½ hour) average voltage value thus minimizing sudden variations in soil-moisture sensor reading because of noise. Further, the preprocessed voltage is converted to soil matric potential (SMP) in kpa value using a calibration chart, the look-up table technique (Doebelin, 2004). The calibration chart stores greenhouse 30

9 Table 1. Summary of the parameters specified for the CWRD method to evaluate the climate control index (CCI) for greenhouse area/grids i VPD climate condition i th range specification (LLi VPD<HL i) Weight (w i) 1 Least optimum low VPD< Sub optimum low 2 VPD<SLL Near optimum low SLL-5 VPD<SLL Most optimum low SLL-3 VPD<SLH Near optimum low SLH+5 VPD<SLH Sub optimum low SLL+10 VPD<SLH Least optimum low SLH+15<VPD 0.1 soil-specific calibration data values relating output voltage to input SMP value as measured by the soil matric potential meter (Irrometer, 2007b). The variation in soil matric potential value from 1 kpa to 70 kpa indicates saturated to very dry state of the soil (Irrometer, 2007a). This functional module indicates the soil matric potential of each grid and its spatial variability. It activates irrigation alarms and also checks the functionality of the soil-moisture sensors and reports malfunctioning of sensors. For the greenhouse crop, the irrigation threshold limit varies linearly from 40 to 25 kpa in accordance with crop-stress risk values (0.3 to 0.5) for greenhouse grids or area. As the SMP value exceeds the high threshold limit, a drip-irrigation system is activated. As soon as the SMP value falls below 5 kpa, indicating water saturated of soil, the irrigation system is switched off. (d) Microclimate assessment tool: The most important point of the work is to characterize the greenhouse area and grids in terms of the parameters that quantitatively describe the quality of the climate control conditions, in terms of degree of optimality and spatial variability of the greenhouse climatic conditions during a certain time or day. To quantify the optimal level of climate conditions, a parameter, the climate control index (CCI), which varies in the range of 0 1 is statistically evaluated for the greenhouse area and grids using the proposed cumulative weighted range duration (CWRD) method. CWRD is based upon the discrete cumulative distribution pattern of GH-crop VPD (area/grids) in different ranges that specify the type of optimal climate conditions. The higher the value of CCI, the better is the climate control condition, coming closer to the optimum range defined by the VPD set limits. CCI is calculated using the formula given by eq. (1): n (1) where i denotes different ranges (1 to n) of GH-crop VPD, for area or grids depicting particular climate condition (Table 1). The i th range is specified in terms of low limit (LL i ) and high limit (HL i ) values as LL i VPD < HL i. LL i and HL i are based upon the variation in the VPD set limit low (SLL) and VPD set limit high (SLH). In eq. (1), w i specifies the weight associated with the i th range of GH-crop VPD for area/grid, depicting the climate control condition. (t d ) i specifies the percentage time duration of GH-crop VPD for area/grids within the i th range. Table 1 summarizes the parameters, specified ranges, and weights for different VPD climate conditions associated with the CWRD method, to evaluate CCI for greenhouse area and grids. The overall spatial variability of the greenhouse climatic conditions is based upon the standard deviation of the grid VPD. The spatial variability of the GH-crop VPD (SV VPD ), is calculated as a square root of the mean of square of the difference between each grid VPD (VPD g ) and average GH-crop VPD given by eq. (2). (2) The lower the value of SV at the given average value of GH-crop VPD, the better is the uniformity of climate condition. The degree of uniformity or the percentage uniformity (U) of climate conditions within the greenhouse 31

10 is measured in terms of degree of scatter of the GH-crop VPD. The degree of scatter is referred as a percentage variation or coefficient of variation, defined as ratio of standard deviation to its mean value in percentage. The percentage uniformity of GH-crop VPD within the greenhouse is given by equation (3) (Mendenhall et al., 2007). (3) where s VPD denotes day mean spatial standard deviation of GH-crop VPD at grids. µ VPD denotes day mean of average GH-crop VPD. s VPD /µ VPD denotes coefficient of variation of GH-crop VPD (e) User interfaces: This is the front end of the software (GEMCS) that allows the user to interact with the GHAN system, feed the required parameters, operate the system, and visualize the results. The user interfaces of GEMCS are the upgraded version of the work indicated in a previous paper (Pahuja et al. 2013). The basic design of the software tool, implemented on the platform of LabVIEW, (Well, 1994), (National Instruments, 2000) is multi-panel, modular and hierarchical. This makes it relative easy, cost and time effective to upgrade software for better information access without changing the underlying structural design of the front panels. The software environment of LabVIEW that supports design flexibility, scalability, and modularity (Ritter, 2002), (Lewis, 2004) is well-suited for creating more innovative changes and customizing the system layout during different development stages of the system/prototype model. In this work, a few changes have been proposed and implemented in user interfaces to cater to new functions of GEMCS, (a) (b) Figure 5. (a)configuration panel GUI of GEMCS that allows the user to select various parameters for online operation of the GHAN system, (b) Soil moisture and irrigation status subpanel of Microclimate monitoring and control panel GUI of GEMCS. Results on the panel indicate the current status of greenhouse soil-moisture at 17:18 on 5Aug15. 32

11 though the outlook appears to be almost the same. Figure 5 (a) shows the Configuration panel GUI of GEMCS that allows the user to configure the GHAN system for effective use in a greenhouse to suit plant requirements and user needs. This panel allows the user to select certain parameters related to climate control equipment components housed in the greenhouse. Based upon the availability of a particular climate control equipment, the user can select option used/unused for the equipment, feed the number of units of climate control devices, power rating of the actuating elements and select auto/manual control mode of operation of the device. The panel also provides the option to select soil-moisture sensors to facilitate irrigation management, along with greenhouse climate control. In additional to these, parameters of climate controller are configurable. Operating modes of both humidifying and dehumidifying controllers can be changed from the default mode as per requirement in different seasons. Apart from this, others parameters related to network deployment, plant related information, set limits of temperature and relative humidity along with irrigation and pesticide schedule time are selected as usual. The user interface has an option to select and view historical data. Figure 5 (b) shows the traditional soil moisture and irrigation status subpanel of the Microclimate monitoring and control panel (Pahuja et al., 2013) GUI. The GUI displays vital information with the respective indicators related to the online operation of greenhouse wireless sensor and actuator network at each instant of time. The results indicated on the panel depicted the current state of the greenhouse at the current time instant 17:18 on 5Aug13 when the GHAN system was deployed in a typical greenhouse. The sub-panel continuously updated the reliable average soil matric potential (SMP) values as measured by in-situ soil matric potential sensors installed at each grid after acquisition time interval of 10 min. It also displayed the average value of SMP in kpa for greenhouse area and its spatial variability together with irrigation alarm status at each grid. Because the greenhouse was well irrigated during the day, soil matrix potential values remained very low (1-2kPa), indicating saturated state of soil-moisture. Furthermore, the spatial variability of soil-moisture within greenhouse area remained very low (0.58). Remote user connectivity tier The server of the GHAN system, housed in a greenhouse control room (50 60 m away from the greenhouse) performs online greenhouse microenvironment monitoring and control operations and provides information to the local users. To provide remote access to the GHAN system, the internet based backend server is enabled on a host PC and greenhouse application software virtual instrument (the VI connectivity network is used because of its easy availability and cost effectiveness). The VI front panel is embedded as a web page with a universal resource locator (URL) that uniquely identifies the VI on the internet using the server IP address (National Instruments, 1994) (Guzman, 2005). The client PC, installed with a LabVIEW runtime engine, uses the URL address to open the webpage of the front panel of the GEMCS software to remotely monitor the operation of the GHAN system. Results and Discussion The GHAN system was deployed and tested in a commercial greenhouse of reasonable size (30 m x 48 m), situated in the tropical region of Northern India in the state of Punjab. Figure 6 shows real deployment of the WSN nodes at different locations within the grids (30 m x 16 m) of the greenhouse to measure the crop (Scotch Bonnet) environment that is grown in the greenhouse for export business. Scotch Bonnet is one of the world's hottest pepper variety crop that is not only used in spices and hot sauces but also has medicinal power. Scotch Bonnet pepper is a high value export crop that is generally grown in greenhouses to generate high return on investment. The crop originated in tropical areas, such as the Caribbean islands and South America, and is also grown in other latitudes where climatic conditions are consistently warm, such as in northern India. For optimum crop growth, temperature and relative humidity conditions vary in the range of C and 60 to 75%, respectively. If it becomes too hot, the plant drops the blooms and fruit. As the temperature increases, the relative humidity requirement of the crop also increases to provide a proper VPD for the crop to transpire well. The crop requires soil that is neither too acidic nor too alkaline, with ph varying in the range of 6 7. Prior to re-plantation of crop in the greenhouse, the 33

12 Pahuja et al. An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Figure 6. Actual deployment of the WSN nodes at different locations within grids of the greenhouse to measure the crop (Stoch Bonette) environment during different stages of crop growth. seeds are grown in trays or pots in nursery in sterilized soil with proper usage of NPK (Nitrogen, Potassium, and Phosphorous) foliage fertilizer. After 4 to 5 weeks, when the seedlings have grown to cm in height, the healthy seedlings are transplanted in the greenhouse in properly prepared soil mulches, maintaining a row distance of cm and a plant-to-plant distance of cm. To meet the water requirement of the crop, it is irrigated using a drip irrigation system once a day or as required by the crop depending upon its growing stage. Water-stress on the crop causes wilting and shed of flowers and fruit. Drip irrigation is recommended for the crop as it not only conserves water but also avoids weed growth and leaching of soil, and minimizes foliage diseases. Therefore, proper climatic conditions are needed for proper crop growth (McGlashan, 2002). In this greenhouse, Scotch Bonnet crops are grown over the year with wide variation in weather conditions. To evaluate the field performance of the GHAN system and the microclimate control dynamic of the greenhouse, testing was performed over several days during the peak 34 day hours (6 7 h) on typical days (with changing weather) covering the crop season from October 2012 to August A few of the field results recorded have been analyzed and the behavior of the system is discussed below. (a) Integrated climate controller performance: The main objective of the GHAN system is to control average GH-crop VPD within desired limits and saving energy, by using an integrated climate. To evaluate the controller performance, the historical trends of different parameters related to the controller operation under auto/manual control of the greenhouse climate were analyzed. Figure 7(a) and (b) show the mean day data indicating controller parameters such as average GH-crop, outside VPD and VPD set limits and VPD controller mean error respectively. Figures 8 (a), (b), (c), and (d) show the percentage duty cycle of different climate control equipment components such as shade screen, roof vents, exhaust fan, and cooling pad respectively. Figure 9 indicates the energy consumption curve

13 (a) (b) Figure 7. (a). Time variations of mean outside VPD, average GH-crop VPD with set limits on under auto/manual climate control on typical days, (b) Time variation of VPD controller mean error under auto/manual climate control on typical days. of the greenhouse on typical days. Under the auto control mode of the GHAN system, the climate controller automatically regulated average GH-crop VPD within or near the set limits. The mean controller error remained within the range of -5 mb to 6 mb, irrespective of the wide variation of exterior weather VPD. The controller operated different actuating devices at different loads to appropriately humidify or dehumidify the greenhouse with saving in energy, thus regulating the average GH-crop VPD. When the humidifying controller was used, the energy consumption remained high (45 65 kwh) and increased with increase in outside VPD. However, under favorable outside weather VPD, the greenhouse climate was regulated with roof vents. Energy consumption (0 3 kwh) and VPD controller mean error both remained very low. Under manual control operation, the climate controller was bypassed. The climate control equipment components were operated as per the user (grower) choice and maintenance problems were taken care in the greenhouse. When outside VPD remained very high (greater than the VPD set limit high) and devices were not appropriately operated, the greenhouse climatic conditions remained undesirable and unregulated. During these days, the average GH-crop VPD remained very high with maximum value varying in the high range of mb. The VPD controller mean error varied in the high range of 5 30 mb. The maximum energy consumption varied in the range of 0 55 kwh. (b) Microclimate Assessment: The knowledge of the 35

14 (a) (b) (c) (d) Figure 8. Time variation of percentage duty cycle of climate control equipment (a) shade screen (b) roof vents (c) exhaust fans (d) cooling pad under different loads and auto/manual climate control on typical days. greenhouse microclimate in terms of GH-crop VPD enhances the reliability of the monitored climate data. In-spite of control of greenhouse climate, GH-crop VPD varied spatially at grids. This provides the basis to assess spatial variability (spatial standard deviation) or percentage uniformity and degree of optimality of climate conditions prevailing within the greenhouse. Figure 10 shows the variation of mean GH-crop VPD at grids G1, G2, and G3 under the auto/manual climate control on typical days. Figures 11 (a) and (b) show the mean spatial variability and percentage uniformity of greenhouse microclimate, respectively. Further, to characterize the grids on the basis of degree of optimality of the overall existing climatic conditions, the parameter climate control index (CCI) was evaluated. Table 2 indicates climate control 36

15 Figure 9. Time variation of maximum energy consumption of greenhouse under auto/manual control on typical days. Figure 10. Time variation of mean GH-crop VPD at grids under auto/manual climate control on typical days. (a) (b) Figure 11. Time variation of (a) spatial variability (b) percentage uniformity of GH-crop VPD under auto/manual climate control on typical days. 37

16 index values and Figures 12 (a) and (b) show the climate control index pattern for greenhouse grids and area under auto/manual control on typical days. When the greenhouse was under auto control and was properly humidified by the operating climate control equipment under a full load, the mean spatial standard deviation varied in the range mb. This value increased with increasing outside VPD. The Table 2. Climate control index values for greenhouse area and grids under auto/manual climate control on the typical days Day Climate control index (CCI) for greenhouse GH area Grid G1 Grid G2 Grid G3 11Oct Oct Nov Nov Dec Jan Jan Feb Feb april May May July July Aug Aug Aug Aug (a) (b) Figure 12. Climate control index pattern (a) GH -grids (b) GH-area under auto/manual climate control on typical days. 38

17 degree of uniformity of the GH-crop VPD varied in the range of 70 85%. Based on the optimal level of climate conditions, the climate control index value (CCI) for greenhouse grids and area varied in the range Most of the time, the GH-crop VPD at grid G3 (west side), near to the cooling pad, remained within optimum range, with a higher value ( ) of CCI compared to the other grids G1 and G2. Under auto control, when the outside VPD was within the optimum limits and only roof vents were operated, the mean spatial variability of the GH-crop VPD remained very low (0.5 2 mb) with high degree of uniformity (85 90%). Most of the time, the GH-crop VPD at the grids remained within or near the set limits, which increased CCI, and it varied in the range of During the manual control operation, selective climate control equipment was used. Hence, the mean spatial variability of the GH-crop VPD varied nonuniformly over a wide range, along with a decrease in CCI value. When the outside VPD was high and the greenhouse was not humidified properly, the mean spatial variability of the GH-crop VPD exceeded the maximum value of 8.90 mb. Most of the time, the climate conditions within the grids remained less than optimal. Hence, CCI for the greenhouse grids and area remained very low (c) Crop health risk measures: One of the preliminary functions of the GHAN system is to estimate the risk on crop health due to adverse climate conditions prevailing within the greenhouse [Pahuja et al, 13]. The crop health risk functional module has a fuzzy-rule based prediction model that analyzes the duration and extent of the high and low VPD conditions prevailing within the greenhouse and provides a cumulative moving average (CMA), a crop-stress risk index (CSRI) value, and a crop disease risk index (CDRI) value, each in the range of 0 to 1. The index value quantifies or estimates the risk that the crop undergoes stress or the chances that disease (fungus pathogen) spread-out. Further, the index value is compared with respective threshold limits to activate irrigation or pesticide alert to safeguard the crop health under such unwanted situations. Figures 13 (a) and (b) show the variation of mean (CMA), crop-stress risk index (CSRI) value in the range of 0 to 1, and relative irrigation alert status for greenhouse grids, respectively, on typical days. Under auto control, the GH-crop VPD at the grids varied and remained within (a) (b) Figure 13. Time variation of crop health parameters for greenhouse grids (a) mean crop- stress risk index value (b) relative time for irrigation alarm under auto/manual climate control on typical days. 39