International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Agriculture is the most important developing sector. But nowadays, it consumes large amounts of water and electricity for the production because of the manual irrigation and scheduling methods [1], [6]. With the increase in population, farmers need to manage more land to irrigate but due to water scarcity, and an increase in power cost that affects more cost for the irrigation [4], [10].

This paper proposes an IoT and Machine Learning-based optimization system by providing water at the right time to the fields with low consumption of the electricity bill [3], [8]. Our proposed solution uses a Random Forest Regression model and it integrates with the IoT for the development. Here, our model works by taking the data from the ESP8266 NodeMCU microcontroller, soil sensor and water level sensor [5], [7]. From these sensors data is taken continuously over a 30-day period and using this we can predict the exact need of water to the fields. All the results are stored in the FireBase while using the wifi.

The implemented model is used for controlling the manual operations for providing water to the fields and it helps to automatically turn ON or OFF when it reaches the required threshold value. This model mainly helps in reducing the water wastage, electricity cost that helps in improving the production. The implemented system achieves the water reduction wastage by 35% and electricity costs by 42% when compared to traditional fixed-irrigation systems, this helps in high model accuracy of (R2) of 0.94 [9], [10].

Keywords: Smart Irrigation, Soil Moisture Sensor, Water Level Sensor, ESP8266 Nodemcu Controller, Resource Efficiency, Energy Scheduling.

Downloads: | DOI: 10.17148/IJARCCE.2026.15234