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

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.

• 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