Autonomous Crop Irrigation System using Artificial Intelligence
Savita Choudhary1, Vipul Gaurav2, Abhijeet Singh3, Susmit Agarwal4
1Savita Choudhary, Department of Computer Science, Sir M. Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India.
2Vipul Gaurav, Department of Computer Science, Sir M. Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India.
3Abhijeet Singh, Department of Computer Science, Sir M. Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India.
4Susmit Agarwal, Department of Computer Science, Sir M. Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India.
Manuscript received on 04 June 2019 | Revised Manuscript received on 12 June 2019 | Manuscript Published on 29 June 2019 | PP: 46-51 | Volume-8 Issue-5S, May 2019 | Retrieval Number: E10100585S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture plays a significant role in the economy and its contribution is based on measurable crop yield which is highly dependent upon irrigation. In a country like India, where agriculture is largely based on the unorganized sector, irrigation techniques and patterns followed are inefficient and often lead to unnecessary wastage of water. This calls for the need of a system which can provide an efficient and deployable solution. In this paper, we provide an Automatic Irrigation System based on Artificial Intelligence and Internet of Things, which can autonomously irrigate fields using soil moisture data. The system is based on prediction algorithms which make use of historic weather data to identify and predict rainfall patterns and climate changes; thereby creating an intelligent system which irrigates the crop fields selectively only when required as per the weather and real-time soil moisture conditions. The system has been tested in a controlled environment with an 80 percent accuracy, thus providing an efficient solution to the problem.
Keywords: Artificial Intelligence, Irrigation, Internet of Things, Prediction Algorithms, Machine Learning, and Water Conservation.
Scope of the Article: Artificial Intelligence and Machine Learning