Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods
Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties,...
Gespeichert in:
Veröffentlicht in: | International journal of food engineering 2020-12, Vol.16 (12) |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 12 |
container_start_page | |
container_title | International journal of food engineering |
container_volume | 16 |
creator | Jaramillo-Acevedo, César Augusto Choque-Valderrama, William Enrique Guerrero-Álvarez, Gloria Edith Meneses-Escobar, Carlos Augusto |
description | Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period. |
doi_str_mv | 10.1515/ijfe-2019-0161 |
format | Article |
fullrecord | <record><control><sourceid>walterdegruyter_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1515_ijfe_2019_0161</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1515_ijfe_2019_01611612</sourcerecordid><originalsourceid>FETCH-LOGICAL-c290t-7e562d167e22708fd8187eb366cbbb2d18a3959697169bc52b0a0f19222cce763</originalsourceid><addsrcrecordid>eNp1kE9LAzEQxYMoWKtXz_kCW5Nsk2y8laJWKPWi5yV_ZteU7aYk20q_vdnWqzAww3u84fFD6JGSGeWUP_ltAwUjVBWECnqFJpRzUZSSV9dowqiaF1yK-S26S2lLCGdSygmClU4J62Ow2gUc_R56yILtsuwbb_XgQ4_NCe-C8R1gB0dvIeFD8n2LnW_9oDvsd7oFvI8hW2dD9w4vNhu8g-E7uHSPbhrdJXj421P09fryuVwV64-39-ViXVimyFBI4II5KiQwJknVuIpWEkwphDXGZKfSpeJKKEmFMpYzQzRpqGKMWQtSlFM0u_y1MaQUoan3MXeLp5qSeoRUj5DqEVI9QsqB50vgR3cDRAdtPJzyUW_DIfa56j_BPKz8BWIjb2Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods</title><source>De Gruyter journals</source><creator>Jaramillo-Acevedo, César Augusto ; Choque-Valderrama, William Enrique ; Guerrero-Álvarez, Gloria Edith ; Meneses-Escobar, Carlos Augusto</creator><creatorcontrib>Jaramillo-Acevedo, César Augusto ; Choque-Valderrama, William Enrique ; Guerrero-Álvarez, Gloria Edith ; Meneses-Escobar, Carlos Augusto</creatorcontrib><description>Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.</description><identifier>ISSN: 2194-5764</identifier><identifier>EISSN: 1556-3758</identifier><identifier>DOI: 10.1515/ijfe-2019-0161</identifier><language>eng</language><publisher>De Gruyter</publisher><subject>feature selection ; fruit classification ; image processing ; non-destructive methods</subject><ispartof>International journal of food engineering, 2020-12, Vol.16 (12)</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c290t-7e562d167e22708fd8187eb366cbbb2d18a3959697169bc52b0a0f19222cce763</citedby><cites>FETCH-LOGICAL-c290t-7e562d167e22708fd8187eb366cbbb2d18a3959697169bc52b0a0f19222cce763</cites><orcidid>0000-0003-4720-6981</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.degruyter.com/document/doi/10.1515/ijfe-2019-0161/pdf$$EPDF$$P50$$Gwalterdegruyter$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.degruyter.com/document/doi/10.1515/ijfe-2019-0161/html$$EHTML$$P50$$Gwalterdegruyter$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,27901,27902,66725,68509</link.rule.ids></links><search><creatorcontrib>Jaramillo-Acevedo, César Augusto</creatorcontrib><creatorcontrib>Choque-Valderrama, William Enrique</creatorcontrib><creatorcontrib>Guerrero-Álvarez, Gloria Edith</creatorcontrib><creatorcontrib>Meneses-Escobar, Carlos Augusto</creatorcontrib><title>Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods</title><title>International journal of food engineering</title><description>Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.</description><subject>feature selection</subject><subject>fruit classification</subject><subject>image processing</subject><subject>non-destructive methods</subject><issn>2194-5764</issn><issn>1556-3758</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LAzEQxYMoWKtXz_kCW5Nsk2y8laJWKPWi5yV_ZteU7aYk20q_vdnWqzAww3u84fFD6JGSGeWUP_ltAwUjVBWECnqFJpRzUZSSV9dowqiaF1yK-S26S2lLCGdSygmClU4J62Ow2gUc_R56yILtsuwbb_XgQ4_NCe-C8R1gB0dvIeFD8n2LnW_9oDvsd7oFvI8hW2dD9w4vNhu8g-E7uHSPbhrdJXj421P09fryuVwV64-39-ViXVimyFBI4II5KiQwJknVuIpWEkwphDXGZKfSpeJKKEmFMpYzQzRpqGKMWQtSlFM0u_y1MaQUoan3MXeLp5qSeoRUj5DqEVI9QsqB50vgR3cDRAdtPJzyUW_DIfa56j_BPKz8BWIjb2Q</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Jaramillo-Acevedo, César Augusto</creator><creator>Choque-Valderrama, William Enrique</creator><creator>Guerrero-Álvarez, Gloria Edith</creator><creator>Meneses-Escobar, Carlos Augusto</creator><general>De Gruyter</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-4720-6981</orcidid></search><sort><creationdate>20201201</creationdate><title>Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods</title><author>Jaramillo-Acevedo, César Augusto ; Choque-Valderrama, William Enrique ; Guerrero-Álvarez, Gloria Edith ; Meneses-Escobar, Carlos Augusto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c290t-7e562d167e22708fd8187eb366cbbb2d18a3959697169bc52b0a0f19222cce763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>feature selection</topic><topic>fruit classification</topic><topic>image processing</topic><topic>non-destructive methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jaramillo-Acevedo, César Augusto</creatorcontrib><creatorcontrib>Choque-Valderrama, William Enrique</creatorcontrib><creatorcontrib>Guerrero-Álvarez, Gloria Edith</creatorcontrib><creatorcontrib>Meneses-Escobar, Carlos Augusto</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of food engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jaramillo-Acevedo, César Augusto</au><au>Choque-Valderrama, William Enrique</au><au>Guerrero-Álvarez, Gloria Edith</au><au>Meneses-Escobar, Carlos Augusto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods</atitle><jtitle>International journal of food engineering</jtitle><date>2020-12-01</date><risdate>2020</risdate><volume>16</volume><issue>12</issue><issn>2194-5764</issn><eissn>1556-3758</eissn><abstract>Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.</abstract><pub>De Gruyter</pub><doi>10.1515/ijfe-2019-0161</doi><tpages>08</tpages><orcidid>https://orcid.org/0000-0003-4720-6981</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2194-5764 |
ispartof | International journal of food engineering, 2020-12, Vol.16 (12) |
issn | 2194-5764 1556-3758 |
language | eng |
recordid | cdi_crossref_primary_10_1515_ijfe_2019_0161 |
source | De Gruyter journals |
subjects | feature selection fruit classification image processing non-destructive methods |
title | Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T22%3A25%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-walterdegruyter_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Hass%20avocado%20ripeness%20classification%20by%20mobile%20devices%20using%20digital%20image%20processing%20and%20ANN%20methods&rft.jtitle=International%20journal%20of%20food%20engineering&rft.au=Jaramillo-Acevedo,%20C%C3%A9sar%20Augusto&rft.date=2020-12-01&rft.volume=16&rft.issue=12&rft.issn=2194-5764&rft.eissn=1556-3758&rft_id=info:doi/10.1515/ijfe-2019-0161&rft_dat=%3Cwalterdegruyter_cross%3E10_1515_ijfe_2019_01611612%3C/walterdegruyter_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |