Objective: The purpose of this study was to maintain the nutritional water temperature in the range of 25-27 °C for Lollo Verde lettuce (Lactuca sativa L.).
Methods: The method was the Fuzzy Logic Mamdani (FLM) with two inputs, i.e. real time clock and temperature. The output was crisp speed PWM with the center of area method.
Results: The results showed that Fuzzy logic was succeeded in reducing water temperature in the NFT system from 28-32 °C to 26-27 °C, with an average delta of 3.5 °C. Fuzzy logic maintained the nutrient water temperature in the Lollo Verde lettuce with an average of 26.57±0.5 °C. Water temperature affected the yield of Lollo Verde lettuce.
Conclusion: The yield of NFT FLM system was better compared to the conventional NFT system

FAUZAN, MOHAMAD NURKAMAL

SAPTARINI, NYI MEKAR

English

Innovare Academic Sciences: E-Journals

Saptarini et al. Int J App Pharm, Vol 13, Special Issue 3, 2021, 23-27 4th International Seminar on Pharmaceutical Science and Technology 2020              | 23 IMPLEMENTATION OF FUZZY LOGIC CONTROLLERS TO MAINTAIN WATER TEMPERATURE IN HYDROPONICS NFT FOR LOLLO VERDE LETTUCE (LACTUCA SATIVA L.) Original Article  MOHAMAD NURKAMAL FAUZAN1, NYI MEKAR SAPTARINI2* 1Applied Bachelor Program of Informatics Engineering, Pos Indonesia Polytechnic, West Java 40151, 2Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, West Java 45363 Email: nyi.mekar@unpad.ac.id Received: 27 Aug 2020, Revised and Accepted: 06 Oct 2020 ABSTRACT Objective: The purpose of this study was to maintain the nutritional water temperature in the range of 25-27 °C for Lollo Verde lettuce (Lactuca sativa L.). Methods: The method was the Fuzzy Logic Mamdani (FLM) with two inputs, i.e. real time clock and temperature. The output was crisp speed PWM with the center of area method. Results: The results showed that Fuzzy logic was succeeded in reducing water temperature in the NFT system from 28-32 °C to 26-27 °C, with an average delta of 3.5 °C. Fuzzy logic maintained the nutrient water temperature in the Lollo Verde lettuce with an average of 26.57±0.5 °C. Water temperature affected the yield of Lollo Verde lettuce. Conclusion: The yield of NFT FLM system was better compared to the conventional NFT system. Keywords: Nutrient film technique, Temperature maintenance, Fuzzy logic mamdani © 2021 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) DOI: https://dx.doi.org/10.22159/ijap.2021.v13s3.04 Journal homepage: https://innovareacademics.in/journals/index.php/ijap  INTRODUCTION Hydroponics is an effective way to preserve water and planting space [1]. Hydroponic culture with the application of nutritional media, has become the second generation of the fastest growing crop production system and agricultural industry [2]. Planting vegetables hydroponic methods lead to economic value. Moreover, the market has begun to grow in accordance with the growing demand [3]. There are several hydroponic systems, including Nutrient Film Technique (NFT) [4]. There are several factors that affect the growth of hydroponic plants, including nutrient levels, acidity and water temperature in the system [5]. Water temperature affects the physiological processes during plant growth and development [6]. Hydroponic growth requires an ideal temperature system container, because the increased oxygen demand is proportional to plant growth [7-9]. An increased temperature around the plant roots will decrease dissolved oxygen levels in water. This causes the increased the pathogen growth, such as Pythium sp. [10-12] The ideal temperature of the nutrient solution is in the range of 18.3-26.6 °C [13] and for tropical plants in the range of 25-30 °C [14, 15]. Bandung, Indonesia, has two seasons, i.e. tropical heat/rain climate. The air temperature is 23.7±0.5 °C, with the lowest temperature is 17.9 °C and the highest is 32.4 °C [16]. The temperature in our hydroponic system ranges from 28 to 32 °C. In this study, the water temperature was cooled by utilizing the air environment temperature [17, 18]. The purpose of this study was to maintain the nutritional water temperature in the range of 25-27 °C for Lollo Verde lettuce (Lactuca sativa L.) [19]. MATERIALS AND METHODS Materials This study was conducted in Sariasih, Bandung, West Java, Indonesia. The study used Lollo Verde lettuce as a plant sample, rockwool, reservoirs, pipes, fans, and Arduino. Methods Hydroponic system design Hydroponic system was modified design which described by Fauzan and Saptarini [20]. Reservoir or container of 20 L for 30 Lollo Verde lettuce filled with 15 L of water (75%). The NFT hydroponic system (fig. 1a) consists of 3 pipes filled with 10 pots that have input-fan and exhaust pipe with exhaust-fan of water flow. The volume of nutrient water per pipe (2.5 inch) with a length of 2 m was filled with 30%, i.e. 2.1 L. Three fans (12v, 0.15A) were parallel arranged to cool the nutrient water temperature with an environment air temperature of 23.7±0.5 °C. Arduino UNO r3 microcontroller (fig. 1b) control fan speed via PWM [21] with input set of DS3231 real time clock (RTC) at 0-23 and DS18B20 waterproof temperature sensor to read temperature values in Celsius units.   (a)      (b) Fig. 1: NFT system design (a) and arduino UNO r3 block (b) International Journal of Applied Pharmaceutics ISSN- 0975-7058                    Vol 13, Special Issue 3, 2021 Saptarini et al. Int J App Pharm, Vol 13, Special Issue 3, 2021, 23-27 4th International Seminar on Pharmaceutical Science and Technology 2020              | 24  Fig. 2: Fuzzy logic controller  Fuzzy Logic Controller (FLC) [22] (fig. 2) was used to read two input parameters, i.e. (a) RTC input to convert hours 0-23 to 3-26, then entered into the time membership (table 1) and (b) temperature sensor input, then entered into the temperature membership (table 2). Both inputs produce outputs according to the PWM membership speed (table 3) through rules (table 4). Output in the form of speed PWM crisps value with the Center of Area (COA) method which is calculated by equation 1 [23].  z0 =∑ μ�wj�.wjnj=1∑ μ�wj�nj=1…. (1) The defuzzified value was denoted as z0. Here wj was indicate the sample element, μ�wj� was the membership function, and n was representing the number of elements in the sample [23].  Table 1: Time membership Membership Time Time conversion Time1 21.00-3.00 21-26 Time2 3.01-20.59 3-21  Table 2: Temperature membership Membership Temperature (°C) Cool 0-25 Normal 25-27 Hot 27-50  Table 3: Speed membership Membership PWM speed off 0 mid 100,128,140 max 255  Table 4: Fuzzy logic rules Membership Time1 (21-26) Time2 (3-21) Cool Off Off normal Off Mid Hot Mid Max  Time inputs were made into two memberships to simplify the calculations. The system was applied rules for time, i.e. if the hour value was less than 3, the hour value was added to 24 to produce its conversion (table 1). Temperature inputs were divided into three memberships (table 2). The PWM speed output was 2 singletons and 1 triangular membership (table 3). Fuzzy logic was used the rules in table 4. The weight comparison of Lollo Verde lettuce between conventional NFT and NFT with FLC systems was done at harvest, i.e. 45 d after the seeding. Statistical analysis  The Python 3.8.2 program was used for data plotting and statistical analysis. RESULTS AND DISCUSSION The Lollo Verde lettuce was used in this set-up to be tested with the hydroponic circuit as shown in fig. 1a to maximize the planting space. The Lollo Verde lettuces were planted on the rockwool slave with 3 hills per slave and implementing the NFT system. In this study, the temperature of nutrient water was considered the factors to be maintained. The temperature maintenance can achieve with compressed refrigeration techniques with the lowest temperatures was 5 °C [24]. Controlling vent fans and growlight can achieve an optimal temperature range of 10 to 35 °C [25]. Light and temperature affected the final internode lengths (FIL) [26]. In the Cold Storage System Temperature study, Fuzzy produced crisp outputs which suitable for hot fan speeds and cold fan speeds of 6.55% and 70%, respectively [27]. The considered factor was the temperature, which plants acceptable, i.e. 25-27 °C for Lollo Verde lettuce [19]. In this study, a microcontroller was used to implement a fuzzy logic-based temperature control system. The purpose of the system was to control the water temperature by regulating the speed of a fan (fig. 1b). There were many ways to describe the variables linguistically, we choose the simple linguistic to simplify the task of the controller using fuzzy logic (fig. 2). Plots in fig. 3 and 4 were obtained from the Python program by entering the membership input and output parameters. Membership inputs, outputs, and rules were obtained from field observations (Sariasih, Bandung) and literature studies. The temperature membership plot was described the group of input values which read by the DS18B20 sensor (fig. 3a). The membership time plot was described the group of input values which read by RTC DS3231 (fig. 3b). The membership output plot was a variation of the PWM speed value from the range 0 to 255 (fig. 4).  (a)      (b) Fig. 3: Input membership of temperature (a) and time (b) Saptarini et al. Int J App Pharm, Vol 13, Special Issue 3, 2021, 23-27 4th International Seminar on Pharmaceutical Science and Technology 2020              | 25  Fig. 4: Speed membership Fig. 5 was obtained from the data recorded for 6 d and randomly taken at different hours from the morning to the night. This design was shown the exhausted water collide with the sucked air from the exhaust fan (2.29 inch x 2 m) with a cross-sectional area of about 0.116 m2 on the surface. The ideal temperature was reached after 6 d the system first runs (fig. 5). After the 6th day, the water temperature showed a stable value, i.e. 26-27 °C. Fig. 6 was obtained from the data recorded from 00.00 to 23.59 (converted from 0 to 26) to the response of fan speed output on the 7th day. Fig. 7 was obtained from recorded data in the form of reading the value of the nutrient water temperature sensor to the fan speed output response on the 7th day. The data in fig. 6 and 7 were carried out simultaneously, but processed with different parameters. Table 5 showed the weight comparison between the conventional NFT and NFT with FLC systems performed at harvest, which 10 plus 35 d from seeding.    Fig. 5: Graph of time versus temperature   Fig. 6: Graph of time versus speed   Fig. 7: The temperature sensor reading and PWM Speed response Saptarini et al. Int J App Pharm, Vol 13, Special Issue 3, 2021, 23-27 4th International Seminar on Pharmaceutical Science and Technology 2020              | 26 At night, temperatures were 17.9±0.2 °C, the exhaust fan did not work, due to the water temperature in the system was ideal (fig. 6). This was in accordance with the Fuzzy logic rules, so the speed output was 0 or a deadly condition. The results showed that even though the water temperatures were relatively stable, sometimes the DS18B20 temperature sensor was increased. Example, at 28.94 °C, the response of the fuzzy system produced a maximum output speed of 255 (fig. 7). The fans were rotated according to the constructed rules (table 4). Fuzzy logic was succeeded in reducing water temperature in the NFT system, from 28-32 °C to 26-27 °C, with an average delta of 3.5 °C. Fuzzy logic was succeeded in maintaining the nutrient water temperature in the Lollo Verde lettuce system, with an average of 26.57±0.5 °C. This temperature was in the recommended temperature range for Lollo Verde lettuce, i.e. 25-27 °C [19].  Table 5: The weight comparison between the conventional NFT and NFT with FLC systems Pot no. NFT (g) NFT+FLC (g) Pot no. NFT (g) NFT+FLC (g) Pot no. NFT (g) NFT+FLC (g) 1 187 193 11 189 199 21 197 193 2 183 192 12 192 199 22 189 193 3 184 198 13 193 196 23 198 188 4 188 185 14 200 184 24 183 195 5 187 200 15 193 196 25 184 182 6 197 184 16 198 189 26 200 183 7 192 198 17 190 193 27 184 199 8 184 198 18 194 182 28 196 184 9 196 201 19 201 198 29 185 200 10 183 200 20 181 185 30 196 189  The weight comparison between the conventional NFT and NFT with FLC systems was performed on Lollo Verde lettuce, 10 d after seeding and transferred to the NFT system. The harvest on the 35th day after planting on the NFT system. The weight of the Lollo Verde lettuce of the conventional NFT and NFT with FLC systems was 190.8±0.4 g and 192.5±0.2 g, respectively (table 5). These results indicate that the NFT with FLC system was 0.9% better than the conventional NFT system. The inlet filters and suitable fan were needed for bigger hydroponic scales. This was aimed to reach the ideal temperature, which far from the ambient temperature. The ideal temperature can be maintained by other heat replacement techniques, such as a peltier or refrigerator and other input parameters, models, and methods. The test place with a different air environment temperature from this study was recommended to change the membership of the input, output, and rules in the program by considering other factors, such as plant type, location, hydroponic system, etc. CONCLUSION The yield of NFT FLM system was better compared to the conventional NFT system. FUNDING  Nil AUTHORS CONTRIBUTIONS All the authors contributed equally.  CONFLICT OF INTERESTS Declared none REFERENCES 1. Tagle S, Benoza H, Pena R, Oblea A. Development of an indoor hydroponic tower for urban farming. Presented at the 6th DLSU Innovation and Technology Fair; 2018. p. 1-7. Available from: https://www.dlsu.edu.ph/wp-content/uploads/pdf/conferences/ditech/proceedings/volume-3/paper-10.pdf [Last accessed on 15 Jul 2020] 2. Chow YN, Lee LK, Zakaria NA, Foo KY. 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IMPLEMENTATION OF FUZZY LOGIC CONTROLLERS TO MAINTAIN WATER TEMPERATURE IN HYDROPONICS NFT FOR LOLLO VERDE LETTUCE (LACTUCA SATIVA L.)

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IMPLEMENTATION OF FUZZY LOGIC CONTROLLERS TO MAINTAIN WATER TEMPERATURE IN HYDROPONICS NFT FOR LOLLO VERDE LETTUCE (LACTUCA SATIVA L.)

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