Govaichelvan, Kumanan N. and Pathmanathan, Dharini and Zainal-Abidin, Rabiatul-Adawiah and Abu, Arpah (2024) Machine learning for major food crops breeding: Applications, challenges, and ways forward. Agronomy Journal, 116 (3). pp. 1112-1125. ISSN 00021962, DOI https://doi.org/10.1002/agj2.21393.
Full text not available from this repository.Abstract
Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Phenotype; Computer Vision; Image Processing |
| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Science > Institute of Biological Sciences Faculty of Science > Institute of Mathematical Sciences |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Aug 2024 04:14 |
| Last Modified: | 22 Aug 2024 04:14 |
| URI: | http://eprints.um.edu.my/id/eprint/46030 |
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