A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction
| Autor: | Yusri Yusup, Nuzhat Khan, Mohamad Anuar Kamaruddin, Bifta Sama Bari, Mamunur Rashid |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial intelligence
010504 meteorology & atmospheric sciences General Computer Science Computer science Yield (finance) palm oil yield Machine learning computer.software_genre 01 natural sciences Field (computer science) Palm oil General Materials Science crop yield prediction 0105 earth and related environmental sciences Strategic planning business.industry Crop yield General Engineering deep learning 04 agricultural and veterinary sciences TK1-9971 Tree (data structure) Identification (information) machine learning Agriculture 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Electrical engineering. Electronics. Nuclear engineering business computer |
| Zdroj: | IEEE Access, Vol 9, Pp 63406-63439 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3075159 |
| Popis: | 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 of an extremely effective model for the prediction of palm oil yields with the most minimal computational difficulty. |
| Databáze: | OpenAIRE |
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