Sensor-Driven Intelligent Irrigation Using Machine Learning for Precision Farming Applications

Abstract

Managing irrigation well is not merely a technical concern. It is tied closely to how carefully we use water, especially in regions where every drop matters during a dry season. The system discussed here explores a fairly practical idea: combining Internet of Things technology with machine learning to help farmers decide when irrigation should actually occur. A small network of low-cost sensors records soil moisture, temperature, and humidity, while an ESP32 microcontroller gathers and transmits the readings. These data points are then interpreted using a K-Nearest Neighbors classifier that predicts a simple outcome, either irrigation is required or it is not. Before training the model, the dataset was cleaned through feature selection, label encoding, and normalization. Using an 80:20 train test split, the model reached about 65.9 percent accuracy. Interestingly, it was better at identifying when irrigation should be activated, which may help prevent under watering. Future improvements could involve adaptive learning and cloud monitoring.