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• W3173500524 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." @default.
• W3173500524 created "2021-07-05" @default.
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• W3173500524 date "2021-06-21" @default.
• W3173500524 modified "2023-09-30" @default.
• W3173500524 title "IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage" @default.
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• W3173500524 doi "https://doi.org/10.7717/peerj-cs.578" @default.
• W3173500524 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8237332" @default.
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