IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage

Ashutosh Bhoi; Rajendra Prasad Nayak; Sourav Kumar Bhoi; Srinivas Sethi; Sanjaya Kumar Panda; Kshira Sagar Sahoo; Anand Nayyar

doi:10.7717/peerj-cs.578

IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage

PeerJ Comput Sci. 2021 Jun 21:7:e578. doi: 10.7717/peerj-cs.578. eCollection 2021.

Authors

Ashutosh Bhoi¹, Rajendra Prasad Nayak¹, Sourav Kumar Bhoi², Srinivas Sethi³, Sanjaya Kumar Panda⁴, Kshira Sagar Sahoo⁵, Anand Nayyar⁶

Affiliations

¹ Department of Computer Science and Engineering, Government College of Engineering (Govt.), Kalahandi, India.
² Department of Computer Science and Engineering, Parala Maharaja Engineering College (Govt.), Berhampur, India.
³ Department of Computer Science Engineering and Applications, Indira Gandhi Institute of Technology (Govt.), Sarang, India.
⁴ Department of Computer Science and Engineering, National Institute of Technology (NIT), Warangal, India.
⁵ Department of Computer Science and Engineering, SRM University, Amaravati, Andhra Pradesh, India.
⁶ Graduate School; Faculty of Information Technology, Duy Tan University, Da Nang, Viet Nam.

Abstract

In the traditional irrigation process, a huge amount of water consumption is required which leads to water wastage. To reduce the wasting of water for this tedious task, an intelligent irrigation system is urgently needed. The era of machine learning (ML) and the Internet of Things (IoT) brings it is a great advantage of building an intelligent system that performs this task automatically with minimal human effort. In this study, an IoT enabled ML-trained recommendation system is proposed for efficient water usage with the nominal intervention of farmers. IoT devices are deployed in the crop field to precisely collect the ground and environmental details. The gathered data are forwarded and stored in a cloud-based server, which applies ML approaches to analyze data and suggest irrigation to the farmer. To make the system robust and adaptive, an inbuilt feedback mechanism is added to this recommendation system. The experimentation, reveals that the proposed system performs quite well on our own collected dataset and National Institute of Technology (NIT) Raipur crop dataset.

Keywords: Artificial Intelligence; Internet of Things; IoT-IIRS; Smart Irrigation.

Grants and funding

This work is financially supported by the Collaborative Research Scheme (CRS) of the National Project Implementation Unit (NPIU), MHRD, Government of India. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.