A Review of the State-of-Art, Limitations, and Perspectives of Machine Vision for Grape Ripening Estimation

Abstract

This work highlights the most recent machine vision methodologies and algorithms proposed for estimating the ripening stage of grapes. Destructive and non-destructive methods are overviewed for in-field and in-lab applications. Integration principles of innovative technologies and algorithms to agricultural agrobots, namely, Agrobots, are investigated. Critical aspects and limitations, in terms of hardware and software, are also discussed. This work is meant to be a complete guide of the state-of-the-art machine vision algorithms for grape ripening estimation, pointing out the advantages and barriers for the adaptation of machine vision towards robotic automation of the grape and wine industry.