Fuzzy Method Design for IoT-Based Mushroom Greenhouse Controlling

The ideal conditions for the oyster mushrooms growth are at a humidity of 6575% and 29-31C during incubation, while the growth of stems should be at a humidity of 7090% 29-32C. This ideal ecosystem is maintained by aeration and manual watering. Still, the results are not optimal in preventing damage to the mycelium during the incubation period, resulting in a decrease in crop yields. Automatic control has not created ideal conditions because air temperature and humidity regulation are only based on fans and sprayers that do not directly affect air conditions. Therefore, we need a method to manipulate the mushroom greenhouse space ecosystem, namely fuzzy logic, the application of fuzzy logic integrated with sensors, actuators, and microcontrollers with the Internet of Things to solve this problem. The results of the installation of fuzzy logic in the mushroom's greenhouse in this system can be seen from the fan's modulation response and the pump's duration. The test results of this control feature can manipulate temperature and humidity. Therefore, the oyster mushroom greenhouse produces an ideal state of 29.8C, the humidity of 68.97% RH, and the production has been proven to be optimal with an average daily harvest of 3.8kg. Keyword—Fuzzy, Mushroom, Internet of Things INTENSIF, Vol.6 No.1 February 2022 ISSN: 2580-409X (Print) / 2549-6824 (Online) DOI: https://doi.org/10.29407/intensif.v6i1.16786 82 INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi


I. INTRODUCTION
In Latin Volvariella volvacea, Oyster mushroom describes a category of mushrooms always cultivated by the population. Oyster mushrooms are tasty and have protein, nutrients, low prices, and people consume them as an alternative food. This oyster mushroom cultivation process requires accuracy and precision tolerance in regulating temperature and humidity that match the natural oyster mushroom habitat ecosystem to create optimal harvest output. One of the valuable roles to get optimal growth of oyster mushrooms is by maintaining air and humidity [1] [2].
Usually, the temperature of oyster mushroom cultivation is maximally divided into two stages, namely: the incubation stage, which requires an air temperature of 29-31C with a humidity of 65-75% [3], the stage of making the body and fruit requires an air temperature of 29-32C with a humidity of 70-90% [4] [5]. In the process of spraying and aeration of the greenhouse, oyster mushroom cultivators still manage the air and humidity manually., so for the incubation phase, the phase of making the mushroom body, it has not been able to achieve maximum conditions, not again when extreme transitions take place. So that it has an impact, the mushroom yields are shrinking because many mycelia are damaged during the incubation phase. Therefore, a fuzzy application procedure is needed to observe effective air temperature and humidity [6].
Fuzzy logic is always used in industry to control sensors, actuators, robotics.
For fuzzy logic to perform optimally in controlling it, a nodeMcu8266 microcontroller is needed, a DHT 11 sensor, an actuator in the form of a fan, and a pump. All devices will be connected to the internet of things (IoT) to facilitate the monitoring process. In previous studies [2][7] [8], fuzzy logic can be integrated with IoT through a microcontroller into an intelligent system. It is reinforced by research [9][10] which states that fuzzy logic effectively regulates room temperature. Tsukamoto fuzzy is used in this study because of its advantages in dealing with the problem of uncertainty [11] and is more stable for multi-sensor data [12]. Several theoretical studies and IoT simulations [13] to produce a unified device require software integration with microcontrollers, sensors, and actuators to be mutually automated. While scientific studies on automatic control instrumentation [14], automatic temperature regulation can optimally increase oyster mushrooms' productivity.

II. RESEARCH METHOD
The system development method used in this research is the Waterfall method. The waterfall method is a method that is carried out from a system carried out one by one and sequentially.
Therefore, if the first step has not been done, so has the second one. If the second step has not been done, so has the third one, and so on. This organized method can anticipate errors in making a program [18].

Figure 1. WATERFALL METHOD
Firstly, researchers plan a control system design for oyster mushrooms cultivation on mushrooms greenhouse using IoT based on fuzzy logic starting from processing preparation.
Preparing tools, designing output variables and input variables, then determining the fuzzy set for each fuzzy variable. The fuzzification process is carried out by collecting data for each input variable, creating a membership function for each fuzzy set based on the smallest data value and the lowest data value for each fuzzy variable. Then, a rule is arranged for each input variable, as shown in Figure   The membership variables in Table 1 have two inputs: temperature and humidity, then for the output of fan aeration and watering motor. The set membership function can be described as in Figure 3.    As a hardware unit in Table.3, the software is needed as the controlling brain to perform automation. This software is built using C language, which is embedded fuzzy in the form of an

III. RESULT AND DISCUSSION
After the entire system on the prototype has been built, as shown in Figure 6, testing is carried out to find out how well fuzzy can manage the oyster mushroom greenhouse ecosystem. In addition, the operational feasibility process between the sensor and microprocessor also needs to be benchmarked.

Figure 6. PROTOTYPE OF HARDWARE AND SOFTWARE A. System Test
Testing the actuator with PWM modulation connected to NodeMcu8266 which has embedded fuzzy logic, then a 12V power supply is provided to the fan motor. Overall, at a temperature range of 23C-35C, fuzzy can provide control input with a fan rotation PWM modulation response to maintain the temperature in the oyster mushroom greenhouse ecosystem. The variable area surface temperature and humidity results are shown in Figure 7.  Table. 5. The results of the fuzzy logic performance in the controller on the daily harvest output are as shown in Figure 7. proven to be optimal with an average daily harvest of 3.8kg. While for further development, it is necessary to create a more compact integrated circuit board path.