Adaptive Temperature And Humidity Control System on Kumbung Mushroom using Fuzzy Neural Network Algorithm

• Published in: IOP conference series. Earth and environmental science Vol. 980; no. 1; p. 012063
• Main Authors: Hartadi, D R, Agustianto, K, Destarianto, P, Wardana, R, Kustiari, T, Wiryawan, I G, Mulyadi, E
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 15.02.2022
• Subjects: Adaptive systems; Algorithms; Back propagation; Back propagation networks; Control systems; Cultivation; Fungiculture; Fuzzy logic; Human resources; Humidity; Humidity control; Moisture control; Mushrooms; Neural networks; Relative humidity; Spraying
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/980/1/012063

National mushroom production in 2018, 2017, 2016, and 2015 were: 31052, 3702, 40915 and 33485 tons. Mushroom cultivation, especially oyster mushrooms, has the potential to be developed more widely in Indonesia because it has economic value and is environmentally friendly. However, the cultivation of oyster mushrooms has challenges. The challenge that arises in the cultivation of oyster mushrooms is to grow well at a temperature of 16 – 30 °C and a relative humidity of 80 – 95%. Environmental conditioning carried out by farmers on average through spraying water in mushroom kumbung manually. The use of manual methods requires high human resources, besides that it is not effective and efficient. This study aims to develop an Adaptive Temperature and Humidity Control System on Kumbung using Backpropagation Neural Network, where to determine the temperature and humidity the system will evaluate the readings of several sensors at once. The system developed is an adaptive system with an accuracy of 97%, so it is can be used to increase the precision of mushroom cultivation, so that production efficiency and increase in mushroom production can be achieved.