Adaptive Fuzzy in Agriculture System

Abstract

Industrial Revolution 4.0 focuses all elements of industry to enter the digital era of governance with a variety of systems, which have been created to provide easy management support, consistency of production, maintain product quality and so on. In agriculture and plantations, the collaborative utilization of detection systems from sensors such as temperature, humidity and light intensity, as well as fuzzy logic algorithm approaches, is expected to produce adaptive operational auxiliary systems. Study for the conditioning of oyster mushroom cultivation room, with research environmental data in the form of temperatures ranging from 15-33 Celsius which will be used for hot and cold classification, humidity 47-99 %RH for classification of high and low humidity conditions and light intensity in the range of values of 80-800 lumens for the classification of dark and bright conditions. The process approaches a combination of rules in forming inference and defuzzification machines that are close to the expected real value. Tsukamoto's method, which was tested in this system research, was able to strengthen the form of fuzzy inference machines to assist in adaptive management of modern cultivation.