Smart Irrigation System And Grow Lights For Tomato Plants Based On The Internet Of Things Using The Fuzzy Logic Method
TL;DR
An Internet of Things-based automated irrigation and lighting system for tomato plants using the Tsukamoto Fuzzy Logic method showed that the natural environment treatment exposed to direct sunlight resulted in stable vegetative growth, while the terrace treatment triggered rapid but inconsistent initial growth.
Abstract
This study was conducted to develop and test an Internet of Things (IoT)-based automated irrigation and lighting system for tomato plants using the Tsukamoto Fuzzy Logic method. The system utilizes an ESP32 microcontroller, with data collected from a CJMCU-TEMT6000 light sensor and a soil moisture sensor, which is then used to control a water pump and UV LED lights via PWM. Monitoring and control can be performed directly via the Firebase platform and an Android app. Test results show that the light sensor has an average error rate of 1.83% with an accuracy of 98.15%, and the soil moisture sensor has an average error rate of 2.15% with an accuracy of 97.85%. The IoT system responds stably with a latency ranging from 3.32 to 5.03 seconds. A 5-week experiment on three plants showed that the natural environment treatment exposed to direct sunlight (Plant C) resulted in stable vegetative growth, while the terrace treatment without direct sunlight exposure (Plant B) triggered rapid but inconsistent initial growth. Meanwhile, the special treatment combining irrigation and grow lights (Plant A) proved most effective in supporting optimal fruit development compared to the other treatments.