Controlling Smart Green House Using Fuzzy Logic Method

p-ISSN: 2356-5314 e-ISSN: 2460-075X International Journal on Smart Material and Mechatronics IJSMM Vol. 2 No. 2 2015 Controlling Smart Green House Using Fuzzy Logic Method Rafiuddin Syam1), Wahyu H. Piarah1) and Budi Jaelani2) 1) Mechanical Engineering, Faculty of Engieering Hasanuddin University, Makassar, Indonesia 2) Technical School 1, Balloci Pangkep, South Sulawesi, Indonesia Email: [email protected] and [email protected] Received: 17 September 2015 / Revised: 15 October 2015 / Accepted: 26 October 2015 / Published online: 10 January 2016 © IJSMM Abstract—To increase agricultural output it is needed a system trace gases; observations of climate change and the problems that can help the environmental conditions for optimum plant associated with those observations; paleoclimatology as it growth. Smart greenhouse allows for plants to grow optimally, relates to previous changes in the greenhouse gases; the because the temperature and humidity can be controlled so that impacts on and interactions with managed and natural no drastic changes. It is necessary for optimal smart greenhouse ecosystems from climate change; policy issues related to needed a system to manipulate the environment in accordance climate change and to the limitation of climate change; history with the needs of the plant. In this case the setting temperature of the study of the greenhouse effect; and some other causes of and humidity in the greenhouse according to the needs of the climate change. Then, in India Sahay and Ghosh[4] explain plant. So using an automated system for keeping such cities across the globe are considered as major anthropogenic environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more sources of greenhouse gases (GHG), yet very few efforts has dynamic in accordance with the input temperature and humidity been made to monitor ambient concentration of GHG in cities, so that the temperature and humidity in the green house plants especially in a developing country like India. maintained in accordance to the reference condition. Based on Andrasko [2] mention that measurement and monitoring the experimental results using fuzzy logic method is effective to issues are emerging at the intcrsections of the project and control the duration of watering and to maintain the optimum national scales, referred to here as monitoring-domain edge temperature and humidity inside the greenhouse Keywords: Temperature, Humidity, Control, Fuzzy Logic method, watering duration I. INRODUCTION The development of agriculture industry in Indonesia increased along with increasing knowledge of agricultural techniques. From traditional farming systems evolved into a modern agricultural system where agricultural product is increased, but by using dangerous pesticides in an effort to increase agricultural output. In other hand will destroy the environment. Then developing agricultural technology by manipulating the climate around the plant called micro-climate. Climate change is happening today has made farmers difficulties for increasing agricultural product. Erratic weather conditions led to the planting and harvesting cannot be determined and estimated. Farmers are difficult to predict the weather in the growing season. Mark and Davel [1] describe an annotated bibliography on issues relating to changes in the concentrations of Earth's greenhouse gases. The areas covered include theory and numerical modelling of climate change; cycles involving carbon dioxide and other radiatively important effects. Adrasko would like to show following actions are necessary to improve existing monitoring capabilities and to help resolve project/national edge effects. Smart greenhouse technology is an alternative solution to control the micro-climatic conditions in the plant. The use of smart greenhouse in the cultivation of plants is one way to provide an environment that is closer to better conditions for plant growth. The use of smart greenhouse so that plants can obtain air temperature and humidity is optimum. David and Murat [3] describe an autonomous computer vision-guided plant sensing and monitoring systemwas designed and constructed to continuously monitor temporal, morphological, and spectral features of lettuce crop growing in a nutrient film technique (NFT) hydroponics system. In other hand, to control the temperature and humidity inside the smart green house would require an automatic controller that can read existing activities in the smart green house. Park et al [5] show the system collected temperature of leaves and humidity on leaves of crop. As well as greenhouse environmental information such as temperature, humidity, etc. Crop diseases, especially, have deep relationship not only with indoor environmental factors but also with humidity lasting 116 International Journal on Smart Material and Mechatronics Vol. 2 No. 2 2015 R. Syam et al, Hasanuddin University, Indonesia p-ISSN: 2356-5314 e-ISSN: 2460-075X time on leaves and temperature of leaves. Accordingly, monitoring crop itself is as important as monitoring indoor environments. In this study the temperature and humidity in the greenhouse can be regulated by watering when the value of high temperatures and low humidity. It is necessary for setting the watering time duration for controlling the temperature and humidity values. The author uses fuzzy logic to produce more crop watering duration dynamic where the temperature and humidity in accordance with the desired plants. Compared with conventional systems, the system is capable of processing input of real values (exact) into a fuzzy scale and process them using the rule base to make the decision which is the output fuzzy system very quickly and accurately. II. CONTROL SYSTEM OF SMART GREENHOUSE Plants can grow properly if schedule for watering is done every day. In the conventional greenhouse, watering is done manually by the owner to perform continuous scheduling watering during the day. This being smart greenhouse that plants watering process is performed automatically in accordance with the desired watering schedule. Schedule watering is done at 07.00 in the morning and at 17.00 in the afternoon. In addition to watering schedules, the greenhouse smart also uses temperature and humidity sensors to maintain the condition of the plants in the green house. Conditioning system for temperature and humidity inside the greenhouse smart refers to climatic factors that affect plant growth. In this case, the parameter is temperature and humidity controlled. In this study, samples were taken where the tomato plants good growing condition is at a temperature of 24o - 32o C. Under the terms of the growth made control systems using temperature and humidity sensors (DHT11) where the sensor detects changes in temperature and humidity that occur in green house. Figure 1. fuzzy logic process for controlling duration of watering Flow diagram for determining the duration of the water pump settings by using fuzzy logic as shown in Figure 1. In regulating the duration of the water pump using fuzzy These are the reference conditions of temperature and humidity conditions in fact, was to get the output of the temperature and humidity sensors in the form of value for the duration of the water pump used mamdani and defuzzification method used is minimized. The output value of the duration of this water pump will then be sent to the microcontroller types arduino uno. By using Fuzzy Inference System (FIS) on software matlab, created a program file in matlab toolbox. Engine FIS will be saved by clicking: File - export. There are two options available i.e., Workspace and File. When selected to Workspace then will appear in Matlab active. At the FIS Editor at setting the two inputs namely Temperature Sensor with conditions of low, medium and high and humidity sensor input with the condition of low, medium and high, as seen in Figure 2. At the FIS Editor at setting the two inputs namely Temperature Sensor with conditions of low, medium and high and humidity sensor input with the condition of low, medium and high, as seen in Figure 2. III. CONTROL DURATION OF GREENHOUSE SPRAYING PUMP SYSTEM USING FUZZY LOGIC METHOD a. Design Fuzzy Logic System for Smart Greenhouse Smart greenhouse has a system of spraying plants that use water pumps. Working of water pump is programmed at microcontoller i.e., Arduino-Uno for controlling an electronic device as a link between the power source to the water pump. Watering duration is controlled by fuzzy logic system. Fuzzy logic method begins by receiving input from a temperature sensor and humidity sensor then their value will be calculated to produce the output as the duration of watering in ths smart green house. In this system the temperature sensors and humidity sensors as inputs, where the sensor will detect temperature and humidity in the smart green house. Then the sensor will give a signal to the microcontroller Arduino-uno which will give the decision of the time duration to electronic devices that connected to a power source of the water pump. Figure 2. Setting parameter masukan dan keluaran pada editor fuzzy 117 International Journal on Smart Material and Mechatronics Vol. 2 No. 2 2015 R. Syam et al, Hasanuddin University, Indonesia p-ISSN: 2356-5314 e-ISSN: 2460-075X The first input is a temperature sensor with conditions of low, medium and high use trimf using the membership function types can be seen in Figure 3. The range values from 0 to 40 express the magnitude of the temperature in the green house. . Figure 5. The output of watering duration of fuzzy logic method Figure 3. Temperature as an input of Fuzzy system A second input of humidity with level of membership function low, medium and high, here, it is using the sigmoid function type for representative of membership function, can be seen in Figure 4. Here, it was chosen three membership function using sigmoid formula. Decisions given by the fuzzy controller is derived from the rules which exist in the database. These decisions are stored as a set rule. Rule-the rule is an if-then statement is intuitive and easy to understand, because only a linguistic statement. There are 9 rules used in setting the duration of the water pump at this Greenhouse. These rules are inserted into the "rule editor" as in Figure 6. Rule which has been included in the rule editor can be seen in the rule option viewer as shown in Figure 7. Rule viewer is used to set the duration of the likelihood of the water pump, the condition is a short, medium or long, such condition depends on the value of the temperature on the output. To set the trends can be done by shifting the center red line for each membership function. Figure 4. Humidity as an input of fuzzy logic system For the duration of the output is time, by using the membership function sigmoid type as shown in Figure 5. The duration of the output condition there are short, medium and long conditions. Figure 6. Setting of rules at fuzzy editor 118 International Journal on Smart Material and Mechatronics Vol. 2 No. 2 2015 R. Syam et al, Hasanuddin University, Indonesia p-ISSN: 2356-5314 e-ISSN: 2460-075X the pump. For the duration of the pump that produced less than 20 seconds otherwise the pump is not running and if the duration of the pump is greater than 20 seconds otherwise the pump running so at 07:00 and 08:00 pump is not running. The duration of the pump obtained at 12.00 where the temperature is 35 ° C and humidity of 35% can be seen in Table 1. Table 1. Result of greenhouse system using Fuzzy Logic Figure 7. Rule viewer at fuzzy editors Shown in Figure 7 shows the value when the temperature of 340 C and humidity indicate 31%, the output obtained output watering for 90.4 seconds. The value of this watering output varies depending on the value of the input temperature and humidity in the green house. Results of rule viewer can be seen in the form of threedimensional surface viewer as shown in Figure 8. Surface viewer is plot a graph of the data released in the process of defuzzification. Simulation of fuzzy logic control shows the results performed on variable temperature and humidity as input variables obtained hour duration of the pump taken at 7:00 to 17:00 as in the table above and can be seen in Figure 9 below. Figure 9. Duration of pump Figure 8. Surface viewer pada editor fuzzy b. Simulasi Fuzzy Logic pada Green house The temperature and the humidity data has been obtained used as input variables in this system to get the duration of watering plants as the output variable. The data has been obtained and then inserted into the rule to get the duration of Figure 10. The temperature before and after watering The results obtained on the duration of the pump tests using fuzzy logic control then carried for watering plants in the 119 International Journal on Smart Material and Mechatronics Vol. 2 No. 2 2015 R. Syam et al, Hasanuddin University, Indonesia greenhouse temperature and humidity obtained every hour can be seen in Figure 10 and Figure 11. p-ISSN: 2356-5314 e-ISSN: 2460-075X [7] Syam, R Lecturer note: An introduction of Sensor Engineering (original title in Bahasa: Seri Buku Ajar Dasar Dasar Teknik Sensor ISBN 978-979-17225-7-5), Engineering Faculty of Unhas. Figure 11. Humidity inside green house before and after watering From the results of the duration of watering with fuzzy logic is done on tomato plants in the green house can keep the temperature and humidity are optimal temperature of 24 ° C 32 ° C and humidity 40% - 80%. It is effective for the duration of watering with fuzzy logic obtained in accordance with the temperature and humidity in the green house. IV. CONCLUSIONS 1. Simulation of watering duration was success using Matlab. The result was duration of watering which inputs of temperature and humidity in smart green house. 2. Watering duration system using fuzzy logic method was effective to keep the temperature on 24 °C – 32 °C dan humidity on 40 % - 80 %. 3. The fuzzy logic method is available to implement at smart greenhouse, which has two or more inputs and single output. DAFTAR PUSTAKA [1] Mark David Handel, James S. Risbey, An annotated bibliography on the greenhouse effect and climate change Climatic Change, 1992, Volume 21, Number 2, Page 97. [2] Kenneth Andrasko, Forest management for greenhouse gas benefits: Resolving monitoring issues across project and national boundaries Mitigation and Adaptation Strategies for Global Change, 1997, Volume 2, Number 2-3, Page 117. [3] David Story, Murat Kacira, Design and implementation of a computer vision-guided greenhouse crop diagnostics system, Machine Vision and Applications, 2015, Page 1. [4] Samraj Sahay, Chirashree Ghosh, Monitoring variation in greenhouse gases concentration in Urban Environment of Delhi, Environmental Monitoring and Assessment, 2012, Page 1. [5] Dae-Heon Park, Beom-Jin Kang, Kyung-Ryong Cho, A Study on Greenhouse Automatic Control System Based on Wireless Sensor Network Wireless Personal Communications, 2009, Page 1. [6] Syam, R., Concept and Technique to build mobile robot, Makassar: Membumi publishing, 2015. (in Bahasa) 120