Cargando…
IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage
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 i...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237332/ https://www.ncbi.nlm.nih.gov/pubmed/34239972 http://dx.doi.org/10.7717/peerj-cs.578 |
_version_ | 1783714708927283200 |
---|---|
author | Bhoi, Ashutosh Nayak, Rajendra Prasad Bhoi, Sourav Kumar Sethi, Srinivas Panda, Sanjaya Kumar Sahoo, Kshira Sagar Nayyar, Anand |
author_facet | Bhoi, Ashutosh Nayak, Rajendra Prasad Bhoi, Sourav Kumar Sethi, Srinivas Panda, Sanjaya Kumar Sahoo, Kshira Sagar Nayyar, Anand |
author_sort | Bhoi, Ashutosh |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8237332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82373322021-07-07 IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage Bhoi, Ashutosh Nayak, Rajendra Prasad Bhoi, Sourav Kumar Sethi, Srinivas Panda, Sanjaya Kumar Sahoo, Kshira Sagar Nayyar, Anand PeerJ Comput Sci Algorithms and Analysis of Algorithms 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. PeerJ Inc. 2021-06-21 /pmc/articles/PMC8237332/ /pubmed/34239972 http://dx.doi.org/10.7717/peerj-cs.578 Text en © 2021 Bhoi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Bhoi, Ashutosh Nayak, Rajendra Prasad Bhoi, Sourav Kumar Sethi, Srinivas Panda, Sanjaya Kumar Sahoo, Kshira Sagar Nayyar, Anand IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
title | IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
title_full | IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
title_fullStr | IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
title_full_unstemmed | IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
title_short | IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
title_sort | iot-iirs: internet of things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237332/ https://www.ncbi.nlm.nih.gov/pubmed/34239972 http://dx.doi.org/10.7717/peerj-cs.578 |
work_keys_str_mv | AT bhoiashutosh iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage AT nayakrajendraprasad iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage AT bhoisouravkumar iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage AT sethisrinivas iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage AT pandasanjayakumar iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage AT sahookshirasagar iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage AT nayyaranand iotiirsinternetofthingsbasedintelligentirrigationrecommendationsystemusingmachinelearningapproachforefficientwaterusage |