Authors: Kewal Manish Patel, Gaurav Tushar Kokate, Durvesh Amit Amale, Shubham Musmade, Atharva Gare
Abstract: Agriculture in India faces challenges such as unpredictable rainfall, improper irrigation planning, and inefficient use of water resources. To address these issues, this paper proposes a Smart Agriculture System that integrates Internet of Things (IoT) sensors with a lightweight Machine Learning model to optimize irrigation. The system collects real-time soil moisture, temperature, humidity, and light intensity data using low-cost sensors such as the soil moisture sensor and DHT11. The data is sent to a cloud platform through an ESP8266/NodeMCU microcontroller for monitoring. A simple ML model, such as Linear Regression or Decision Tree, predicts the required watering level based on sensor patterns. When moisture falls below the predicted threshold, the system automatically activates a water pump and sends an alert to the farmer’s mobile dashboard. The proposed solution reduces water wastage, increases crop health, and facilitates precision agriculture. This work demonstrates how IoT and ML together can support sustainable agricultural practices, contributing to UN Sustainable Development Goals (SDG-2 and SDG-12). The prototype is easy to implement, low-cost, and scalable for real-world applications.
DOI: https://doi.org/10.5281/zenodo.18595980
