Predictive Plant Irrigation Management System Using Machine Learning

Precision farming makes use of cutting-edge technology such as automation, machine learning (ML), and the internet of things (IoT) to improve agricultural productivity by making data-driven decisions. Based on real-time environmental and soil sensor data, this study proposes an automated irrigation categorization system that makes use of IoT and machine learning to forecast crop irrigation needs. The system integrates Raspberry Pi, Arduino, and IoT sensors, enabling automated decision-making after being trained on a dataset sourced from Kaggle. Random Forest Classifier (RFC) had the highest accuracy, with 98.02% and the least root mean square error (RMSE) of 0.14, according to a comparison of machine learning models, including RFC, Gradient Boosting Classifier (GBC), and XGBoost. The findings show that ensemble learning approaches increase classification precision, guaranteeing ideal soil moisture content and effective water allocation. The suggested method provides a scalable and effective way to maximize crop productivity, reduce water waste, and optimize irrigation. In the future, the proposed system helps in irrigation management and support resource-efficient and sustainable precision agriculture, future developments will investigate weather forecasting models and deep learning approaches.