Intelligent irrigation system using ML and IoT

International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VIII, Issue V, May 2023|ISSN 2454-6194

Intelligent irrigation system using ML and IoT

Manjunath G S1, Sudarshan2
Dept. of ISE 1, Dept. of ECE 2, BNMIT, Bengaluru, Karnataka, India
DOI: https://doi.org/10.51584/IJRIAS.2023.8501
Received: 13 April 2023; Revised: 26 April 2023; Accepted: 01 May 2023; Published: 22 May 2023

Abstract: To realize 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 fulfill the demands of specific applications. A platform which is IoT-based which is used for smart irrigation with a adaptable 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 customized analytical methods for 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 use of precious water, which results in waste of water. An intelligent irrigation system is in desperate need to decrease the wastage of water during this tiresome process. Using Machine learning (ML) and the Internet of Things (IoT),it is possible to develop an intelligent system that can accomplish this operation automatically and with minimum human intervention. An system which is enables using IoT and trained using ML is highly recommended and is suggested in this paper for optimum water consumption with minimal farmer interaction. In agriculture, IoT sensors are used to capture exact field and environmental data. The data being collected is transferred and kept in a cloud-based server that uses machine learning to evaluate the data and provide irrigation recommendations.

Keywords: IoT, ML, cloud, irrigation, water

I. Introduction

In our country where agriculture contributes for 60-70 percent of the GDP, there is a pressing necessity to modernize conventional agricultural techniques to increase yield. 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 drop in the amount of water on the planet. Water scarcity is currently one of the world ' s most pressing issues. Every sector requires water. Water is highly essential for 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 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, most research are focusing on creating creative water utilization in irrigation. The Internet of Things (IoT) which was a concept earlier is now developed to a stage of implementation for real-world applications. Since then, the technological and application hurdles have been considerable.

IoT platforms permit complex real-time control systems by stacking communication infrastructure, hardware, software, logical approaches, and application knowledge. Recognizing the expected effects of IoT on systems is one of the most difficult technical problems because IoT allows systems to become amalgams of services, combining elements as services. The development of the system will become a dynamic mix of interoperable, off-the-shelf services, and the logic of the system will become the integration of the service accordingly.

An intelligent IoT-based irrigation system with an efficient machine learning algorithm is being developed to help farmers overcome rain uncertainty and increase 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.

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