Method for the recovery of images in databases of Rice grains from visual content
This paper presents a method for detecting and identifying defects in polished rice grains from their scanned image using an expert system. The sample used is designed to contain specimens with the most common defects. Digital image processing techniques were used to identify different types of visi...
- Autores:
-
Varela Izquierdo, Noel
Silva, Jesus
Marin Gonzalez, Fredy
Palencia-Domínguez, Pablo
Hernandez Palma, Hugo
Pineda, Omar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7799
- Acceso en línea:
- https://hdl.handle.net/11323/7799
https://doi.org/10.1016/j.procs.2020.03.097
https://repositorio.cuc.edu.co/
- Palabra clave:
- Imágenes
calidad
escáner
Arroz
Sistemas de Visión Artificial (SVA)
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Method for the recovery of images in databases of Rice grains from visual content |
| title |
Method for the recovery of images in databases of Rice grains from visual content |
| spellingShingle |
Method for the recovery of images in databases of Rice grains from visual content Imágenes calidad escáner Arroz Sistemas de Visión Artificial (SVA) |
| title_short |
Method for the recovery of images in databases of Rice grains from visual content |
| title_full |
Method for the recovery of images in databases of Rice grains from visual content |
| title_fullStr |
Method for the recovery of images in databases of Rice grains from visual content |
| title_full_unstemmed |
Method for the recovery of images in databases of Rice grains from visual content |
| title_sort |
Method for the recovery of images in databases of Rice grains from visual content |
| dc.creator.fl_str_mv |
Varela Izquierdo, Noel Silva, Jesus Marin Gonzalez, Fredy Palencia-Domínguez, Pablo Hernandez Palma, Hugo Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Varela Izquierdo, Noel Silva, Jesus Marin Gonzalez, Fredy Palencia-Domínguez, Pablo Hernandez Palma, Hugo Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Imágenes calidad escáner Arroz Sistemas de Visión Artificial (SVA) |
| topic |
Imágenes calidad escáner Arroz Sistemas de Visión Artificial (SVA) |
| description |
This paper presents a method for detecting and identifying defects in polished rice grains from their scanned image using an expert system. The sample used is designed to contain specimens with the most common defects. Digital image processing techniques were used to identify different types of visible defects in rice grains that affect the quality of the sample. The proposed method has advantages over manual identification such as reduced analysis times, repeatability of results, eliminates subjectivity in identification, records and stores information, uses easily accessible equipment and has a relatively low cost. |
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2021 |
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2021-01-29T19:03:36Z |
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2021 |
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https://doi.org/10.1016/j.procs.2020.03.097 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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1 Sánchez L., Vásquez C., Viloria A., Rodríguez Potes L. Greenhouse Gases Emissions and Electric Power Generation in Latin American Countries in the Period 2006–2013 Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018) 2 Aitkenhead M.J., Dalgetty I.A., Mullins C.E., Strachan N.J.C. Weed and crop discrimination using image analysis and artificial intelligence methods Computers and Electronics in Agriculture, 39 (3) (2003) 3 Singh Samrendra K., Vidyarthi Sriram K., Tiwari Rakhee Machine learnt image processing to predict weight and size of rice kernels Journal of Food Engineering, 274 (2020), p. 109828 4 Olivera Y., Machado R., Pozo P.P. Características botánicas y agronómicas de especies forrajeras importantes del género Brachiaria Pastos y Forrajes, 29 (1) (2006) 5 Abbaspour-Gilandeh Yousef, et al. "A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars Agronomy, 10 (1) (2020), p. 117 6 SenseFly. (2014). El dron para la agricultura de precisión. Disponible en: https://www.sensefly.com/fileadmin/user_upload/sensefly/documents/bro chures/eBee_Ag_es.pdf [Consultado el 29 de septiembre del 2015]. 7 Cruz M.C., Rodríguez L.C., Vi R.G. Evaluación agronómica de cuatro nuevas variedades de pastos Revista de Producción Animal, 25 (1) (2013) 8 Erenturk K., Erenturk S., Tabil L.G. A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network Computers and Electronics in Agriculture, 45 (1-3) (2004) 9 Hernández D., Carballo M., Reyes F. Reflexiones sobre el uso de los pastos en la producción sostenible de leche y carne de res en el trópico Pastos y Forrajes, 23 (4) (2000) 10 Hernández R.M., Pérez V.R., Caraballo E.A.H. Predicción del rendimiento de un cultivo de plátano mediante redes neuronales artificiales de regresión generalizada Publicaciones en Ciencias y Tecnología, 6 (1) (2012) 11 Ashtiani Seyed-Hassan Miraei, Abbas Rohani, Mohammad Hossein Aghkhani Soft computing-based method for estimation of almond kernel mass from its shell features. Scientia Horticulturae, 262 (2020), p. 109071 12 Liundi, Nicholas, et al. "Improving Rice Productivity in Indonesia with Artificial Intelligence." 2019 7th International Conference on Cyber and IT Service Management (CITSM). Vol. 7. IEEE, 2019. 13 Abbaspour-Gilandeh Yousef, et al. "A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars Agronomy, 10 (1) (2020), p. 117 14 Lezama, O.B.P., Izquierdo, N.V., Fernández, D.P., Dorta, R.L.G., Viloria, A., Marín, L.R.: Models of Multivariate Regression for Labor Accidents in Different Production Sectors: Comparative Study. In International Conference on Data Mining and Big Data, Springer, Cham, 10942 (1), 43-52 (2018). 15 Suárez J.A., Beatón P.A., Escalona R.F., Montero O.P. Energy, environment and development in Cuba Renewable and Sustainable Energy Reviews, 16 (5) (2012), pp. 2724-2731 16 Sala S., Ciuffo B., Nijkamp P. A systemic framework for sustainability assessment Ecological Economics, 119 (1) (2015), pp. 314-325 17 Singh R.K., Murty H.R., Gupta S.K., Dikshit A.K. An overview of sustainability assessment methodologies Ecological indicators, 9 (2) (2009), pp. 189-212 18 Varela N., Fernandez D., Pineda O., Viloria A. Selection of the best regression model to explain the variables that influence labor accident case electrical company Journal of Engineering and Applied Sciences, 12 (1) (2017), pp. 2956-2962 19 Yao Z., Zheng X., Liu C., Lin S., Zuo Q., Butterbach-Bahl K. Improving rice production sustainability by reducing water demand and greenhouse gas emissions with biodegradable films Scientific reports, 7 (1) (2017), pp. 1-12 |
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Varela Izquierdo, NoelSilva, JesusMarin Gonzalez, FredyPalencia-Domínguez, PabloHernandez Palma, HugoPineda, Omar2021-01-29T19:03:36Z2021-01-29T19:03:36Z2021https://hdl.handle.net/11323/7799https://doi.org/10.1016/j.procs.2020.03.097Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper presents a method for detecting and identifying defects in polished rice grains from their scanned image using an expert system. The sample used is designed to contain specimens with the most common defects. Digital image processing techniques were used to identify different types of visible defects in rice grains that affect the quality of the sample. The proposed method has advantages over manual identification such as reduced analysis times, repeatability of results, eliminates subjectivity in identification, records and stores information, uses easily accessible equipment and has a relatively low cost.Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Silva, JesusMarin Gonzalez, FredyPalencia-Domínguez, Pablo-will be generated-orcid-0000-0003-3679-6015-600Hernandez Palma, HugoPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencesciencedirect.com/science/article/pii/S1877050920305354#!ImágenescalidadescánerArrozSistemas de Visión Artificial (SVA)Method for the recovery of images in databases of Rice grains from visual contentArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 Sánchez L., Vásquez C., Viloria A., Rodríguez Potes L. Greenhouse Gases Emissions and Electric Power Generation in Latin American Countries in the Period 2006–2013 Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)2 Aitkenhead M.J., Dalgetty I.A., Mullins C.E., Strachan N.J.C. Weed and crop discrimination using image analysis and artificial intelligence methods Computers and Electronics in Agriculture, 39 (3) (2003)3 Singh Samrendra K., Vidyarthi Sriram K., Tiwari Rakhee Machine learnt image processing to predict weight and size of rice kernels Journal of Food Engineering, 274 (2020), p. 1098284 Olivera Y., Machado R., Pozo P.P. Características botánicas y agronómicas de especies forrajeras importantes del género Brachiaria Pastos y Forrajes, 29 (1) (2006)5 Abbaspour-Gilandeh Yousef, et al. "A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars Agronomy, 10 (1) (2020), p. 1176 SenseFly. (2014). El dron para la agricultura de precisión. Disponible en: https://www.sensefly.com/fileadmin/user_upload/sensefly/documents/bro chures/eBee_Ag_es.pdf [Consultado el 29 de septiembre del 2015].7 Cruz M.C., Rodríguez L.C., Vi R.G. Evaluación agronómica de cuatro nuevas variedades de pastos Revista de Producción Animal, 25 (1) (2013)8 Erenturk K., Erenturk S., Tabil L.G. A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network Computers and Electronics in Agriculture, 45 (1-3) (2004)9 Hernández D., Carballo M., Reyes F. Reflexiones sobre el uso de los pastos en la producción sostenible de leche y carne de res en el trópico Pastos y Forrajes, 23 (4) (2000)10 Hernández R.M., Pérez V.R., Caraballo E.A.H. Predicción del rendimiento de un cultivo de plátano mediante redes neuronales artificiales de regresión generalizada Publicaciones en Ciencias y Tecnología, 6 (1) (2012)11 Ashtiani Seyed-Hassan Miraei, Abbas Rohani, Mohammad Hossein Aghkhani Soft computing-based method for estimation of almond kernel mass from its shell features. Scientia Horticulturae, 262 (2020), p. 10907112 Liundi, Nicholas, et al. "Improving Rice Productivity in Indonesia with Artificial Intelligence." 2019 7th International Conference on Cyber and IT Service Management (CITSM). Vol. 7. IEEE, 2019.13 Abbaspour-Gilandeh Yousef, et al. "A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars Agronomy, 10 (1) (2020), p. 11714 Lezama, O.B.P., Izquierdo, N.V., Fernández, D.P., Dorta, R.L.G., Viloria, A., Marín, L.R.: Models of Multivariate Regression for Labor Accidents in Different Production Sectors: Comparative Study. In International Conference on Data Mining and Big Data, Springer, Cham, 10942 (1), 43-52 (2018).15 Suárez J.A., Beatón P.A., Escalona R.F., Montero O.P. Energy, environment and development in Cuba Renewable and Sustainable Energy Reviews, 16 (5) (2012), pp. 2724-273116 Sala S., Ciuffo B., Nijkamp P. A systemic framework for sustainability assessment Ecological Economics, 119 (1) (2015), pp. 314-32517 Singh R.K., Murty H.R., Gupta S.K., Dikshit A.K. An overview of sustainability assessment methodologies Ecological indicators, 9 (2) (2009), pp. 189-21218 Varela N., Fernandez D., Pineda O., Viloria A. Selection of the best regression model to explain the variables that influence labor accident case electrical company Journal of Engineering and Applied Sciences, 12 (1) (2017), pp. 2956-296219 Yao Z., Zheng X., Liu C., Lin S., Zuo Q., Butterbach-Bahl K. Improving rice production sustainability by reducing water demand and greenhouse gas emissions with biodegradable films Scientific reports, 7 (1) (2017), pp. 1-12PublicationORIGINALMethod for the recovery of images in databases of Rice grains from visual content.pdfMethod for the recovery of images in databases of Rice grains from visual content.pdfapplication/pdf96887https://repositorio.cuc.edu.co/bitstreams/5c6f8b86-cce0-4330-8390-627ec9d71e79/downloadb3fbcd842e7728957dde5e914efdde58MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/44aa27a2-c881-4559-b4e9-841b7ab696aa/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/b58d9ffa-995f-46bb-a043-fe8dcfe6830f/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMethod for the recovery of images in databases of Rice grains from visual content.pdf.jpgMethod for the recovery of images 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