Nutrition Control System In Nutrient Film Technique (NFT) Hydroponics With Convolutional Neural Network (CNN) Method

Abstract

Limited land makes agriculture increasingly squeezed by settlements, trade, and industry, and this can be seen in unresolved human growth. The existence of hydroponic technology is a solution for farming on narrow land. Hydroponics is the cultivation of plants by utilizing water as a planting medium, so it doesn’t need to use a large area. The cultivation of hydroponic planting requires a particular method. The nutritional needs and pH of hydroponic plants must be maintained so that a nutrient control system can facilitate the controlling and monitoring of nutrients so that they remain according to to plant needs. In this research, an automatic control system for nutrition and pH was created in the Nutrient Film Technique (NFT) hydroponic model. The control system process uses a microcontroller with the Convolutional Neural Network (CNN) method. Overall the system can carry out the nutrition control process automatically without using a laptop. The system runs entirely within the microcontroller. The control system uses the CNN method with the input parameters pH, nutrition, and time as well as output the duration of the flame up, pH down, food, and water pump to reach the set target value. The results of the research that has been done show that the error value for healthy control is 3.35% and 0.98% for pH control.