Next Article in Journal
Nutrient Use in Vertical Farming: Optimal Electrical Conductivity of Nutrient Solution for Growth of Lettuce and Basil in Hydroponic Cultivation
Next Article in Special Issue
Physical, Anatomical, and Biochemical Composition of Skins Cell Walls from Two Grapevine Cultivars (Vitis vinifera) of Champagne Region Related to Their Susceptibility to Botrytis cinerea during Ripening
Previous Article in Journal
Improvement of Nutraceutical Value of Parsley Leaves (Petroselinum crispum) upon Field Applications of Beneficial Microorganisms
Previous Article in Special Issue
Rootstocks Impact Yield, Fruit Composition, Nutrient Deficiencies, and Winter Survival of Hybrid Cultivars in Eastern Canada
Review

Machine Vision for Ripeness Estimation in Viticulture Automation

HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Massimo Bertamini
Horticulturae 2021, 7(9), 282; https://doi.org/10.3390/horticulturae7090282
Received: 30 July 2021 / Revised: 28 August 2021 / Accepted: 31 August 2021 / Published: 3 September 2021
(This article belongs to the Special Issue Advances in Viticulture Production)
Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined. View Full-Text
Keywords: machine vision; grape ripeness estimation; image analysis; precision agriculture; agrobots; harvesting robot machine vision; grape ripeness estimation; image analysis; precision agriculture; agrobots; harvesting robot
Show Figures

Figure 1

MDPI and ACS Style

Vrochidou, E.; Bazinas, C.; Manios, M.; Papakostas, G.A.; Pachidis, T.P.; Kaburlasos, V.G. Machine Vision for Ripeness Estimation in Viticulture Automation. Horticulturae 2021, 7, 282. https://doi.org/10.3390/horticulturae7090282

AMA Style

Vrochidou E, Bazinas C, Manios M, Papakostas GA, Pachidis TP, Kaburlasos VG. Machine Vision for Ripeness Estimation in Viticulture Automation. Horticulturae. 2021; 7(9):282. https://doi.org/10.3390/horticulturae7090282

Chicago/Turabian Style

Vrochidou, Eleni, Christos Bazinas, Michail Manios, George A. Papakostas, Theodore P. Pachidis, and Vassilis G. Kaburlasos 2021. "Machine Vision for Ripeness Estimation in Viticulture Automation" Horticulturae 7, no. 9: 282. https://doi.org/10.3390/horticulturae7090282

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop