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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216305:26620/22:PU145673

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

MATERIALS TODAY-PROCEEDINGS

ISBN

—

ISSN

2214-7853

e-ISSN

—

Počet stran výsledku

8

Strana od-do

6671-6678

Název nakladatele

Elsevier

Místo vydání

Amsterdam

Místo konání akce

Indie

Datum konání akce

9. 4. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000836487800025