Implementation of IoT-Based Weather Prediction System Using Machine Learning for Irrigation Optimization in Peatland Oil Palm Plantations

Abstract

Oil palm plantations on tropical peatlands require adaptive irrigation management due to unstable environmental conditions. This study proposes an IoT-based environmental monitoring and rainfall prediction system for irrigation optimization in oil palm plantations located in Palembang, South Sumatra, Indonesia. The proposed system integrates multiple environmental sensors with an ESP32 microcontroller for real-time monitoring and wireless data transmission. Historical environmental data were processed using the Long Short-Term Memory (LSTM) algorithm for short-term rainfall prediction under multiple forecasting scenarios. Experimental results demonstrated that the proposed model achieved stable learning performance and acceptable rainfall prediction accuracy. Overall, the proposed system shows potential for supporting smart irrigation management in peatland plantation areas.