International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 2108 Smart Irrigation System using Machine Learning and IoT Bharath D.A. 1 , S. Amith Nadig 2 , Manjunath G.S. 3 1 VIII Semester, Dept. of ISE, BNMIT 2 VIII Semester, Dept. of ISE, BNMIT 3 Asst. Professor, Dept. of ISE, BNMIT, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - To realise IoT promise in commercial-scale applications, integrated Internet of Things (IoT) platforms are required. The key challenge is to make the solution flexible enough to fulfil the demands of specific applications. A IoT- based platform for smart irrigation with a flexible design is created so that it allows developers to quickly link IoT and machine learning (ML) components to create application solutions. The design allows for a variety of customised analytical methods to precision irrigation, allowing for the advancement of machine learning techniques. Impacts on many stakeholders may be predicted, including IoT specialists, who would benefit from easier system setup, and farmers, who will benefit from lower costs and safer crop yields. The typical irrigation procedure necessitates a large quantity of water use, which results in water waste. An intelligent irrigation system is desperately needed to decrease water waste during this tiresome process. Machine learning (ML) and the Internet of Things (IoT) have made it possible to develop an intelligent system that can accomplish this operation automatically and with minimum human intervention. An IoT-enabled ML-trained recommendation system is suggested in this paper for optimum water consumption with minimal farmer interaction. In the agriculture field, IoT sensors are used to capture exact ground and environmental data. The collected data is transferred and kept in a cloud-based server that uses machine learning to evaluate the data and provide irrigation recommendations. Key Words: IoT, ML, cloud, irrigation, water. 1. INTRODUCTION In India, where agriculture accounts for 60-70 percent of the GDP, there is a pressing need to modernise traditional agricultural techniques in order to increase output. The groundwater table is lowering day by day as a result of uncontrolled water usage; lack of rainfall and shortage of land water also contribute to a decrease in the amount of water on the planet. Water scarcity is currently one of the world's most pressing issues. Water is required in every sector. Water is also necessary in our daily lives. Agriculture is one of the industries that need a lot of water. Water wastage is a serious issue in agriculture. Every time there is a surplus of water, it is distributed to the fields. Climate change and its consequences are widely explored in academic studies on water resources and agriculture. Because of the potential repercussions of global warming, water adaptation methods are being considered to assure water availability for food and human production as well as ecosystem sustainability. Additionally, the safety of water for human consumption and return to the environment must be maintained. Increased water shortages, poor quality of water, higher water and soil salinity, loss of biodiversity, increased irrigation needs, and the expense of emergency and corrective action are all possible risks from climate change. As a result of these factors, a rising number of research are focusing on creating creative water utilisation in irrigation. The Internet of Things (IoT) has now progressed from a concept to being implemented in real- world applications. Since then, the technological and application hurdles have been considerable. IoT platforms enable complex real-time control systems by layering communication infrastructure, hardware, software, analytical approaches, and application knowledge. Recognizing the expected IoT consequences on systems is one of the most difficult technological issues, because IoT allows systems to become service mashups, combining items as services. System development will become dynamic plug- and-play interoperable service composition, and system logic will become service orchestration as a result. An IoT-based smart irrigation system with an effective machine learning algorithm is developed to assist farmers in overcoming the uncertainty of rainfall and increasing production. This model provides a superior irrigation decision-making model. This research presents a Machine Learning (ML) strategy for successfully regulating irrigation and enhancing agricultural yield as a result. 2. LITERATURE REVIEW Goldstein et al. (2017) [1] suggested a recommendation- based irrigation management system that combined machine learning with agronomic knowledge. According to the system, the best regression model with 93 percent accuracy, and the best classifier model with 95 percent accuracy, Gradient Boosted Regression Trees and Boosted Tree Classifier, provide superior irrigation prediction decisions than the linear regression model. To assist the agronomist in making better selections, the models were trained with eight separate sets of features. The Internet of