Deployment of intelligent irrigation monitoring system with Android app for machine learning prediction.

Abstract

Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially considering that agriculture accounts for a substantial portion of freshwater usage. The inadequate monitoring resources in agriculture lead to unnecessary wastage of water, affecting crop growth and the water supply. This challenge has spurred us to focus on the agricultural domain, aiming to conserve water by imparting intelligence into irrigation systems by converging IoT and machine learning technologies. Our study has proposed a Smart Irrigation System (SIS), designed to operate remotely, leveraging open-source IoT platforms. Utilizing IoT and machine learning methodologies can effectively optimize water usage in irrigation practices. The designed system comprises hardware and software tools, where comprehensive data is monitored through an application interface. The sensor's interface with a 32-bit microcontroller is used to gather data such as environmental temperature, humidity, and soil moisture and temperature. The user (farmer) continuously receives real-time updates about the condition of the farmland through the Blynk app. The app triggers a notification advising the user to start or stop watering based on pre-set threshold values of soil moisture. Additionally, users can control the water pump remotely through the app. The data collected from the deployed smart irrigation system was used to train a machine learning algorithm incorporating weather parameters and soil temperature to predict soil moisture content. This trained model aims to offer guidance for future assessments of unknown soil samples based on the weather conditions.