Monitoring of Soil Humidity and Temperature usingIoT and AI for Remote Management

Monitoring of Soil Humidity and Temperature usingIoT and AI for Remote Management

Real-time, Soil, Humidity, Temperature, Palm, Linear Regression

Efficient environmental monitoring and irrigation management are critical challenges in tropical oil palm plantations due to high humidity, temperature variability, and large cultivation areas. This study presents an Internet of Things (IoT)–based monitoring and decision-support system integrated with a lightweight linear regression model to optimize plantation management.This study presents an IoT-based monitoring and decision-support system for tropical oil palm plantations that integrates calibrated environmental sensors with a lightweight linear regression model to enable real-time irrigation management. Deployed over three months on a 50-hectare plantation, the system achieved a 98.7% data transmission success rate and strong predictive performance (R²= 0.89, MSE = 0.45), delivering below-threshold humidity notifications with 92–94% accuracy and an average latency of 4.3 seconds via an Android application. Field results demonstrate that data-driven irrigation reduced water usage by 23%, increased freshfruit bunch (FFB) yield by 12%, and lowered manual inspection and labor costs, confirming the s

Enggar Hero Istoto1, Evvin Faristasarir2, Sirlus Andreanto Jasman Duli3, Peprizal4, Ade Putra Maulana5, Bradika Almandin Wisesa6, Catur Pebriandani7, Danny Pranata8, Mardinata Indra Kristianto

https://ijicom.respati.ac.id/index.php/ijicom/article/view/213/136

International Journal of Informatics and Computation (IJICOM)

2025

Fajar bagus W

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English

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