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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31799/ http://umpir.ump.edu.my/id/eprint/31799/1/A%20comprehensive%20review%20of%20crop%20yield%20prediction%20using%20machine.pdf |
| _version_ | 1848823859787071488 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:03:50Z |
| format | Article |
| id | ump-31799 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:03:50Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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. (Published) https://doi.org/10.1109/ACCESS.2021.3075159 https://doi.org/10.1109/ACCESS.2021.3075159 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/31799/ http://umpir.ump.edu.my/id/eprint/31799/ http://umpir.ump.edu.my/id/eprint/31799/ http://umpir.ump.edu.my/id/eprint/31799/1/A%20comprehensive%20review%20of%20crop%20yield%20prediction%20using%20machine.pdf |