Plant Growth Prediction Model of Red Chili (Capsicum annuum L.) by Different Manipulation Environment

Abstract

Several factors influence plant growth, including sun intensity, nutrient content, soil moisture, temperature, genes, and hormones. Many studies have been carried out in constructing plant growth models to simulate plant growth in different treatments. This study aims to develop a mathematical model with a linear regression approach and an artificial neural network. This research method used an experimental design using three treatments consisting of control (T1), 50% shade (T2), and 80% shade (T3). Each treatment had five replications of the chili plant. The tools and materials used were red chili (Capsicum annuum L.) seeds of 30 DAP, a greenhouse of 3 x 3 meters, a drip irrigation control system, 25 x 30 cm polybags, and fertile soil media. The results showed that linear regression models of the 1 st and 2 nd order could be used to predict plant growth with an average RMSE value of 1.53. In contrast, the use of artificial neural networks showed a smaller RMSE value of 0.12 which means that the artificial neural network method was better at predicting plant growth.