Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction-A Review.

Abstract

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 glasshouse) assessment. 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.

Plant phenotyping relevance

果実の成熟度・収穫時期・収量を非破壊的に推定する植物フェノタイピング手法を中心に扱うレビューであり、方法論的役割が明確です。

Code and data availability

This is a review article on non-destructive fruit ripeness assessment. The supplied blocks contain no public phenotype/trait datasets, plant images, sensor data, author analysis code, or trained models specific to this paper; all cited studies are prior work, and no data or code availability statements appear.

No evidence-backed public reproduction asset is currently recorded.