research-article

An efficient IoT based smart farming system using machine learning algorithms

Authors:

Nermeen Gamal Rezk,

Ezz El-Din Hemdan,

Abdel-Fattah Attia,

Ayman El-Sayed,

Mohamed A. El-RashidyAuthors Info & Claims

Multimedia Tools and Applications, Volume 80, Issue 1

Pages 773 - 797

https://doi.org/10.1007/s11042-020-09740-6

Published: 01 January 2021 Publication History

Abstract

This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop productivity and drought for proficient decision support making in IoT based smart farming systems. The crop productivity and drought predictions is very important to the farmers and agriculture’s executives, which greatly help agriculture-affected countries around the world. Drought prediction plays a significant role in drought early warning to mitigate its impacts on crop productivity, drought prediction research aims to enhance our understanding of the physical mechanism of drought and improve predictability skill by taking full advantage of sources of predictability. In this work, an intelligent method based on the blend of a wrapper feature selection approach, and PART classification technique is proposed for crop productivity and drought predicting. Five datasets are used for estimating the proposed method. The results indicated that the projected method is robust, accurate, and precise to classify and predict crop productivity and drought in comparison with the existing techniques. From the results, the proposed method proved to be most accurate in providing drought prediction as well as the productivity of crops like Bajra, Soybean, Jowar, and Sugarcane. The WPART method attains the maximum accuracy compared to the existing supreme standard algorithms, it is obtained up to 92.51%, 96.77%, 98.04%, 96.12%, and 98.15% for the five datasets for drought classification, and crop productivity respectively. Likewise, the proposed method outperforms existing algorithms with precision, sensitivity, and F Score metrics.

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Cited By

Kumar Kasera RGour SAcharjee T(2024)A comprehensive survey on IoT and AI based applications in different pre-harvest, during-harvest and post-harvest activities of smart agricultureComputers and Electronics in Agriculture10.1016/j.compag.2023.108522216:COnline publication date: 12-Apr-2024
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Mahalingam NSharma P(2024)An intelligent blockchain technology for securing an IoT-based agriculture monitoring systemMultimedia Tools and Applications10.1007/s11042-023-15985-883:4(10297-10320)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15985-8
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Index Terms

An efficient IoT based smart farming system using machine learning algorithms
1. Applied computing
2. Computing methodologies
  1. Machine learning

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Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 80, Issue 1

Jan 2021

1589 pages

ISSN:1380-7501

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2021

Accepted: 26 August 2020

Revision received: 07 August 2020

Received: 12 May 2020

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Cited By

Kumar Kasera RGour SAcharjee T(2024)A comprehensive survey on IoT and AI based applications in different pre-harvest, during-harvest and post-harvest activities of smart agricultureComputers and Electronics in Agriculture10.1016/j.compag.2023.108522216:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.compag.2023.108522
Mahalingam NSharma P(2024)An intelligent blockchain technology for securing an IoT-based agriculture monitoring systemMultimedia Tools and Applications10.1007/s11042-023-15985-883:4(10297-10320)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15985-8
Zou LSun QWu ZWang X(2024)Lightweight detection method for industrial gas leakage based on improved YOLOv7-tinyMultimedia Systems10.1007/s00530-024-01502-w30:5Online publication date: 27-Sep-2024
https://dl.acm.org/doi/10.1007/s00530-024-01502-w
Hemdan EEssa YShouman MEl-Sayed AMoustafa A(2023)An efficient IoT based smart water quality monitoring systemMultimedia Tools and Applications10.1007/s11042-023-14504-z82:19(28827-28851)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14504-z
Ramakrishnam Raju SDappuri BRavi Kiran Varma PYachamaneni MVerghese DMishra M(2022)Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application SystemJournal of Nanomaterials10.1155/2022/44355912022Online publication date: 1-Jan-2022
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Venkatesan SLim JCho Y(2022)A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart FarmsComputational Intelligence and Neuroscience10.1155/2022/26486952022Online publication date: 1-Jan-2022
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Dahane ABenameur RKechar B(2022)An IoT Low-Cost Smart Farming for Enhancing Irrigation Efficiency of Smallholders FarmersWireless Personal Communications: An International Journal10.1007/s11277-022-09915-4127:4(3173-3210)Online publication date: 1-Dec-2022
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Perales Gómez ÁLópez-de-Teruel PRuiz AGarcía-Mateos GBernabé García GGarcía Clemente F(2022)FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farmingCluster Computing10.1007/s10586-021-03489-925:3(2163-2178)Online publication date: 1-Jun-2022
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