Bo Li
Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction—a review
Li, Bo; Lecourt, Julien; Bishop, Gerard
Authors
Julien Lecourt
Gerard Bishop
Abstract
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre-and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshousassessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.
Citation
Li, B., Lecourt, J., & Bishop, G. (2018). Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction—a review. Plants, 7(1), Article 3. https://doi.org/10.3390/plants7010003
Journal Article Type | Review |
---|---|
Acceptance Date | Jan 8, 2018 |
Online Publication Date | Jan 10, 2018 |
Publication Date | Mar 1, 2018 |
Deposit Date | Feb 24, 2020 |
Publicly Available Date | Feb 24, 2020 |
Journal | Plants |
Electronic ISSN | 2223-7747 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Issue | 1 |
Article Number | 3 |
DOI | https://doi.org/10.3390/plants7010003 |
Keywords | pre-harvest; ripeness; image analysis; machine learning; fruit phenotyping |
Public URL | https://uwe-repository.worktribe.com/output/4583488 |
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Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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