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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...

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Autores principales: Bhoi, Ashutosh, Nayak, Rajendra Prasad, Bhoi, Sourav Kumar, Sethi, Srinivas, Panda, Sanjaya Kumar, Sahoo, Kshira Sagar, Nayyar, Anand
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
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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.
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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
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