Ripening stage classification of Coffea arabica L. var. Castillo using a Machine learning approach with the electromechanical impedance measurements of a contact device

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081723%3A_____%2F22%3A00560331" target="_blank" >RIV/68081723:_____/22:00560331 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26620/22:PU145673

Result on the web

<a href="http://dx.doi.org/10.1016/j.matpr.2022.04.669" target="_blank" >http://dx.doi.org/10.1016/j.matpr.2022.04.669</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.matpr.2022.04.669" target="_blank" >10.1016/j.matpr.2022.04.669</a>

Result language

angličtina

Original language name

Ripening stage classification of Coffea arabica L. var. Castillo using a Machine learning approach with the electromechanical impedance measurements of a contact device

Original language description

The new agricultural prototypes or devices based on deep physical insights as the frequency and vibrational analysis must aid the gap between the plants and their fruit mechanical properties and time dependence. The present study describes a non-destructive method to classify coffee fruits (Coffea arabica L. var. Castillo) according to their ripening stage using high-frequency vibrations, Ripetech. This device's main advantages of this novel proposal are the physical insights of its electro-mechanical response, design functionality to hold fruits, and operability. For this purpose, a vibration technique was developed through electromechanical impedance evaluation of a piezo device that stimulates coffee fruits by holding tweezers. This methodology was planned to conduct electrical impedance measurements and correlate these with the ripening stage. Then, experimental vibration tests were directed between the frequency spectrum 5 and 50 kHz to obtain a spectral vibration database for a total sample of 45 fruits, 15 per each proposed ripening stage. Statistical indexes based on the root mean square (RMS) enabled the implementation of a classifier based on Machine Learning (the Naive-Bayes algorithm). The method proposed in this study tested the effectiveness of classifying fruits in three stages of ripening: unripe, semi-ripe, and ripe/over-ripe. This work evidences an alternative for classifying coffee fruit differently from the traditional operations. As a relevant result, each fruit has exposed its characteristic response signal, which is correlated with the ripening stage. Furthermore, this technology could help select ripe fruits more efficiently, leading to a feasible complementing selective harvesting technology development process. Copyright (C) 2022 Elsevier Ltd. All rights reserved.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

MATERIALS TODAY-PROCEEDINGS

ISBN

—

ISSN

2214-7853

e-ISSN

—

Number of pages

8

Pages from-to

6671-6678

Publisher name

Elsevier

Place of publication

Amsterdam

Event location

Indie

Event date

Apr 9, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000836487800025