A NEW MODEL FOR HYDROPONIC LETTUCE NUTRITION ADAPTIVE CONTROL SYSTEM BASED ON FUZZY LOGIC SUGENO METHOD USING ESP32 - Andi Baso Kaswar; Ridwan Daud Mahande; Jasruddin Daud Malago (Jurnal Teknik Informatika (Jutif), 2023)
In the last few years, the terms Smart Agriculture, Smart Farming, Urban Farming, or Precision Farming have been increasingly recognized and growing rapidly. Hydroponics is one part that is currently a trend, both in industrial or household scale businesses and hobbies. One of the most important things to consider in maintaining the quality of hydroponic plant growth is the concentration of nutrients in the water. A series of studies have been conducted to improve the quality of hydroponic plants. However, the developments that have been carried out have not focused on optimal nutritional control. The previous hydroponic plant nutrition control system still used conventional methods, namely the use of a rule base with firm values ​​, and did not consider the quantity and quality of water. Therefore, this study proposes a new model for an adaptive control system for hydroponic lettuce nutrition based on the Fuzzy Logic Sugeno method using ESP32. The fuzzy logic Sugeno method is used to create a new model of the inference system for determining the amount of nutrient dosage based on supporting data obtained from sensors installed on hydroponic growing media. Compared with the conventional method, the resulting test results show that the proposed method can adapt the amount of added nutrients, provide optimal nutrient addition output, and prevent excess nutrient additions that can potentially accumulate toxic ions in water that degrade water quality.