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Machine learning for major food crops breeding: Applications, challenges, and ways forward

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 plan...

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Main Authors: Govaichelvan, Kumanan N., Pathmanathan, Dharini, Zainal-Abidin, Rabiatul-Adawiah, Abu, Arpah
Format: Article
Published: Wiley 2024
Subjects:
Q Science (General)
T Technology (General)
Online Access:http://eprints.um.edu.my/46030/
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spelling my.um.eprints.460302024-08-22T04:14:05Z http://eprints.um.edu.my/46030/ Machine learning for major food crops breeding: Applications, challenges, and ways forward Govaichelvan, Kumanan N. Pathmanathan, Dharini Zainal-Abidin, Rabiatul-Adawiah Abu, Arpah Q Science (General) T Technology (General) 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. Wiley 2024-05 Article PeerReviewed 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 <https://doi.org/10.1002/agj2.21393>. 10.1002/agj2.21393
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Govaichelvan, Kumanan N.
Pathmanathan, Dharini
Zainal-Abidin, Rabiatul-Adawiah
Abu, Arpah
Machine learning for major food crops breeding: Applications, challenges, and ways forward
description 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.
format Article
author Govaichelvan, Kumanan N.
Pathmanathan, Dharini
Zainal-Abidin, Rabiatul-Adawiah
Abu, Arpah
author_facet Govaichelvan, Kumanan N.
Pathmanathan, Dharini
Zainal-Abidin, Rabiatul-Adawiah
Abu, Arpah
author_sort Govaichelvan, Kumanan N.
title Machine learning for major food crops breeding: Applications, challenges, and ways forward
title_short Machine learning for major food crops breeding: Applications, challenges, and ways forward
title_full Machine learning for major food crops breeding: Applications, challenges, and ways forward
title_fullStr Machine learning for major food crops breeding: Applications, challenges, and ways forward
title_full_unstemmed Machine learning for major food crops breeding: Applications, challenges, and ways forward
title_sort machine learning for major food crops breeding: applications, challenges, and ways forward
publisher Wiley
publishDate 2024
url http://eprints.um.edu.my/46030/
_version_ 1811682115673128960
score 13.211869

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