POGHAR

Variable Selection on Reflectance NIR Spectra for the Prediction of TSS in Intact Berries of Thompson Seedless Grapes – Agronomy 2022, vol. 12, no. 9, 2113

Abstract Fourier-transform near infrared (FT-NIR) reflection spectra of intact berries of the grape variety Thompson seedless were used to predict total soluble solids (TSS) content. From an initial dataset, 12 subsets were considered by applying variable selection to extract the reflectance values at wavenumbers most correlated to the chemometrically measured TSS content. The datasets were processed by both multiple linear regression (MLR) and partial least squares (PLS) methods towards predicting the TSS content from the reflection values of each spectrum.…

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A non-destructive method for grape ripeness estimation using Intervals’ Numbers (INs) techniques – Agronomy, vol. 12, no. 7, 1564, 2022

C. Bazinas, E. Vrochidou, T. Kalampokas, A. Karampatea, V. G. Kaburlasos, “A non-destructive method for grape ripeness estimation using Intervals’ Numbers (INs) techniques”, Agronomy, vol. 12, no. 7, 1564, 2022. https://doi.org/10.3390/agronomy12071564

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A review of the state-of-art, limitations and perspectives of machine vision for grape ripening estimation – EFITA International Conference 2021

E. Vrochidou, C. Bazinas, G. A. Papakostas, T. Pachidis, V. G. Kaburlasos, “A review of the state-of-art, limitations and perspectives of machine vision for grape ripening estimation”, 13th EFITA (European Federation for Information Technology in Agriculture, Food and Environment) International Conference, 25-26 May 2021. In: MDPI Engineering Proceedings 2021, 9 (1), 2; https://www.mdpi.com/2673-4591/9/1/2

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Machine vision for ripeness estimation in viticulture automation – Horticulturae, vol. 7, iss. 9, 282, 2021

E. Vrochidou, C. Bazinas, M. Manios, G. A. Papakostas, T. P. Pachidis, V. G. Kaburlasos, “Machine vision for ripeness estimation in viticulture automation”, Horticulturae, vol. 7, iss. 9, 282; https://www.mdpi.com/2311-7524/7/9/282 (Open Access). (Special Issue on “Advances in Viticulture Production”. Guest Editor: Massimo Bertamini)

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Grape stem detection using regression convolutional neural networks – Computers and Electronics in Agriculture, vol. 186, p. 106220, 2021

Τ. Kalampokas, Ε. Vrochidou, G. A. Papakostas, T. Pachidis, and V. G. Kaburlasos, “Grape stem detection using regression convolutional neural networks,” Comput. Electron. Agric., vol. 186, p. 106220, Jul. 2021, doi: 10.1016/j.compag.2021.106220.

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