An Artificial Neural Network Model for Predicting the Growth of Lettuce Crops in Hydroponic System with Root Zone Cooling

Abstract

Abstract This study aimed to develop an artificial neural network model for predicting the growth of lettuce grown hydroponically. The model was developed using parameters including root zone temperature, air temperature, relative humidity, nutrient concentration, nutrient acidity, solar radiation, leaf area, and leaf number to estimate fresh weight in the next two days. The result of this study was an artificial neural network model with 13 hidden layers, 100 iterations of epoch, coefficient of determination (R2) of 0.93, and root mean squared error of 3.72 gram. This research concluded that the model performs well in predicting lettuce growth using fresh weight development during cultivation.