Smart hydroponic agriculture using genetic algorithm based k-nearest neighbors

Abstract

In this research, researcher has implemented supervised machine learning, namely k-nearest neighbor (k-NN) which is optimized using genetic algorithms, and the internet of things (IoT) on the nutrient film technique (NFT) hydroponic system. The aim of this research is to improve the accuracy of classification of nutrient and light conditions in NFT system, and evaluating the harvest of hydroponic farming. The dataset was obtained by observing and recording nutritional and light conditions using sensors for 35 days during the growing period of lettuce in the NFT system, thus obtaining 1,680 data. Then, a training dataset is created based on that dataset. The system architecture is divided into 3 parts, namely the sensor system, data processing, and actuator system. The conclusion of this research is the IoT can be used to monitor the nutritional and light conditions of NFT system in real time and automatic control actions can be carried out using actuators controlled by the Raspberry Pi, the impact of applying the k-NN algorithm and the genetic algorithms is the accuracy of classifying nutritional and light conditions is 92%, the lettuce in a NFT system controlled by the system grow better than the lettuce in a NFT system controlled manually.