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A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop...

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Main Authors: Rashid, Mamunur, Bari, Bifta Sama, Yusri, Yusup, Mohamad Anuar, Kamaruddin, Khan, Nuzhat
Format: Article
Language:English
Published: IEEE 2021
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/31799/1/A%20comprehensive%20review%20of%20crop%20yield%20prediction%20using%20machine.pdf
http://umpir.ump.edu.my/id/eprint/31799/
https://doi.org/10.1109/ACCESS.2021.3075159
https://doi.org/10.1109/ACCESS.2021.3075159
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spelling my.ump.umpir.317992021-11-10T04:42:31Z http://umpir.ump.edu.my/id/eprint/31799/ A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction Rashid, Mamunur Bari, Bifta Sama Yusri, Yusup Mohamad Anuar, Kamaruddin Khan, Nuzhat T Technology (General) TK Electrical engineering. Electronics Nuclear engineering An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development. IEEE 2021-04-22 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/31799/1/A%20comprehensive%20review%20of%20crop%20yield%20prediction%20using%20machine.pdf Rashid, Mamunur and Bari, Bifta Sama and Yusri, Yusup and Mohamad Anuar, Kamaruddin and Khan, Nuzhat (2021) A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access, 9 (9410627). 63406 -63439. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2021.3075159 https://doi.org/10.1109/ACCESS.2021.3075159
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Rashid, Mamunur
Bari, Bifta Sama
Yusri, Yusup
Mohamad Anuar, Kamaruddin
Khan, Nuzhat
A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
description An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development.
format Article
author Rashid, Mamunur
Bari, Bifta Sama
Yusri, Yusup
Mohamad Anuar, Kamaruddin
Khan, Nuzhat
author_facet Rashid, Mamunur
Bari, Bifta Sama
Yusri, Yusup
Mohamad Anuar, Kamaruddin
Khan, Nuzhat
author_sort Rashid, Mamunur
title A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
title_short A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
title_full A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
title_fullStr A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
title_full_unstemmed A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
title_sort comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
publisher IEEE
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/31799/1/A%20comprehensive%20review%20of%20crop%20yield%20prediction%20using%20machine.pdf
http://umpir.ump.edu.my/id/eprint/31799/
https://doi.org/10.1109/ACCESS.2021.3075159
https://doi.org/10.1109/ACCESS.2021.3075159
_version_ 1717093635982360576
score 13.211869

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