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)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Artificial intelligence; Crop yield prediction; Deep learning; Machine learning; Palm oil yield; |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 10 Nov 2021 04:42 |
| Last Modified: | 10 Nov 2021 04:42 |
| URI: | http://umpir.ump.edu.my/id/eprint/31799 |
| Download Statistic: | View Download Statistics |
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