International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
AgroSense: A Real-Time Smart Irrigation Monitoring System Using IoT and Machine Learning Mr. Kishan S1, Mr. Bharath S B2, Mr. Abhishek3 , Dr. Balaji S4, Dr. Nayana G.Bhat5 1,2,3Students, Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India 4Professor,
Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India
5Assistant Professor, Computer Science and Engineering, Jyothy Institute of Technology, Bangalore, India
---------------------------------------------------------------------***--------------------------------------------------------------------without compromising output and enables farmers to Abstract - AgroSense is an intelligent irrigation make better decisions.
monitoring device designed to help farmers make faster, more informed irrigation decisions. The system collects real-time data such as temperature, humidity, electric conductivity, and soil moisture using a field-deployed network of sensors. The data are transferred to a server, where a decision tree-based machine learning algorithm determines whether or not irrigation is required. Users can then access the insights using a mobile and web application, which allows them to view historical data in charts, monitor current conditions, and get timely irrigation warnings. AgroSense, developed utilizing cutting-edge technology and with a focus on simplicity and sustainability, is intended to reduce water waste and enable more informed farming via smart automation.
1.1 Motivation Mismanagement of agricultural water is still a major problem, with common consequences such as crop loss, low yields, and long-term environmental damage. Farmers continue to use conventional irrigation techniques, which may result in excessive or insufficient usage of water since they do not take into consideration the actual soil or weather conditions. This not only wastes valuable resources but also has an impact on farm productivity and profitability. According to research, smart irrigation systems based on the Internet of Things can reduce water consumption by up to 40% while maintaining or even increasing crop yields. AgroSense, our solution, uses a sensor-driven, real-time decision-support system to solve these issues. AgroSense uses machine learning to provide precise irrigation recommendations by taking temperature, soil moisture, and other environmental factors into account. In addition to improving water efficiency and enabling sustainable agriculture, this technology gives farmers practical insights.
Key Words: Smart Irrigation, IoT Sensors, Machine Learning, Flutter, Real-time Data, Web and Mobile Application, Precision Agriculture, Decision trees, Flask , PostgreSQL, LH-SLTHPHECR sensor, USR-W610.
1.INTRODUCTION In most countries, especially developing ones like India where the vast majority of people depend on agriculture for their livelihood, it continues to be the foundation of both economic stability and food security. Technology has advanced in other fields, but agriculture has lagged behind in adopting new solutions, primarily due to ignorance and resource constraints. Water management stands out as one of the most pressing issues facing farmers these day. It is more important than ever to control water use in agriculture because of the rising strain that climate change is placing on freshwater resources and the unpredictable weather patterns it produces. Traditional irrigation techniques, including hand watering or schedule-based systems, frequently lead to either excessive or insufficient irrigation, which negatively impacts crop productivity and health. Conventional approaches typically overlook timesensitive aspects including temperature, humidity, and soil moisture, which results in water waste and, in some cases, crop damage. These inefficiencies may have significant socioeconomic repercussions in regions with limited water supplies. This calls for a more intelligent, datadriven approach to irrigation—one that reduces waste
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1.2 Objective This study seeks to develop and deploy a smart irrigation system that utilizes the potential of IoT and machine learning to optimize water management in agriculture. The primary objective is to create a system that is capable of providing precise and timely irrigation suggestions based on real-time soil data received from IoT-based sensors. One of the most significant goals is assessing the efficiency of a decision tree algorithm in interpreting this sensor data and making irrigation decisions. Another significant goal is the creation of an easy-to-use mobile application that enables farmers to see important soil parameters and obtain irrigation recommendations in an intuitive and user-friendly manner. In general, this research adds to the emerging body of precision agriculture by offering a real-world and effective solution to maximize water use and enhance crop yields through smart automation.
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