Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial

En el desarrollo de esta investigación, se implementa un prototipo de banda transportadora para la selección de ciruela, en la cual se instala una sección de sensado compuesta de una cámara contenida en un ambiente de luminosidad controlada. Las imágenes adquiridas por el sistema de sensado, fueron...

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Autores:
Bautista Gordo, Cristian Javier
Fonseca Cala, Yesid Aldemar
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2021
Institución:
Universidad Santo Tomás
Repositorio:
Repositorio Institucional USTA
Idioma:
spa
OAI Identifier:
oai:repository.usta.edu.co:11634/33770
Acceso en línea:
http://hdl.handle.net/11634/33770
Palabra clave:
Artificial Intelligence
Plum Selection
Image Classification
Computer Vision
Machine Learning
Inteligencia Artificial
Selección de Ciruela
Clasificación de Imágenes
Visión por computador
Aprendizaje automático
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 2.5 Colombia
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oai_identifier_str oai:repository.usta.edu.co:11634/33770
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network_name_str Repositorio Institucional USTA
repository_id_str
dc.title.spa.fl_str_mv Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
title Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
spellingShingle Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
Artificial Intelligence
Plum Selection
Image Classification
Computer Vision
Machine Learning
Inteligencia Artificial
Selección de Ciruela
Clasificación de Imágenes
Visión por computador
Aprendizaje automático
title_short Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
title_full Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
title_fullStr Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
title_full_unstemmed Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
title_sort Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial
dc.creator.fl_str_mv Bautista Gordo, Cristian Javier
Fonseca Cala, Yesid Aldemar
dc.contributor.advisor.none.fl_str_mv Pardo, Camilo
Ávila, Adolfo
dc.contributor.author.none.fl_str_mv Bautista Gordo, Cristian Javier
Fonseca Cala, Yesid Aldemar
dc.contributor.corporatename.spa.fl_str_mv Universidad Santo Tomás seccional Tunja
dc.subject.keyword.spa.fl_str_mv Artificial Intelligence
Plum Selection
Image Classification
Computer Vision
Machine Learning
topic Artificial Intelligence
Plum Selection
Image Classification
Computer Vision
Machine Learning
Inteligencia Artificial
Selección de Ciruela
Clasificación de Imágenes
Visión por computador
Aprendizaje automático
dc.subject.proposal.spa.fl_str_mv Inteligencia Artificial
Selección de Ciruela
Clasificación de Imágenes
Visión por computador
Aprendizaje automático
description En el desarrollo de esta investigación, se implementa un prototipo de banda transportadora para la selección de ciruela, en la cual se instala una sección de sensado compuesta de una cámara contenida en un ambiente de luminosidad controlada. Las imágenes adquiridas por el sistema de sensado, fueron sometidas, por una parte; a algoritmos de Visión por Computador y Deep Learning, orientados a la extracción de características y, por otra parte; a algoritmos de Machine Learning y Deep Learning, orientados a la clasificación del fruto en tres categorías, definidas por sus características morfológicas y deficiencias nutricionales (afectaciones visuales). Las primeras pruebas para llegar a una clasificación satisfactoria, se realizaron aplicando sobre las imágenes múltiples técnicas de Visión por Computador como: Detección de bordes de Canny, operaciones morfológicas, segmentación de fondo por umbrales, entre otras técnicas que permiten hacer ingeniería de características, las cuales dieron paso a una estructura de clasificación condicional. Posteriormente, se hicieron pruebas con árboles de decisión, máquinas de soporte vectorial, perceptrón multicapa y K-Vecino más cercano (KNN). Finalmente, se implementó una estructura de red neuronal convolucional (CNN).
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-04-23T22:47:35Z
dc.date.available.none.fl_str_mv 2021-04-23T22:47:35Z
dc.date.issued.none.fl_str_mv 2021-04-22
dc.type.local.spa.fl_str_mv Trabajo de grado
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.drive.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.citation.spa.fl_str_mv Bautista, C., & Fonseca, Y. (2021). Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial.
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11634/33770
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad Santo Tomás
dc.identifier.instname.spa.fl_str_mv instname:Universidad Santo Tomás
dc.identifier.repourl.spa.fl_str_mv repourl:https://repository.usta.edu.co
identifier_str_mv Bautista, C., & Fonseca, Y. (2021). Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial.
reponame:Repositorio Institucional Universidad Santo Tomás
instname:Universidad Santo Tomás
repourl:https://repository.usta.edu.co
url http://hdl.handle.net/11634/33770
dc.language.iso.spa.fl_str_mv spa
language spa
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Aggarwal, C. C. (2018). Neural Networks and Deep Learning. In Neural Networks and Deep Learning. https://doi.org/10.1007/978-3-319-94463-0
Alegre, E. G., Pajares, M., & de la Escalera Hueso, A. (2016). Conceptos y métodos en visión por computador. Retrieved from https://books.google.com.co/books?id=9g9UAQAACAAJ
Bautista, C. J., Aldemar Fonseca, Y., & Pardo-Beainy, C. (2020). Plum selection system using computer vision. 2020 IEEE ANDESCON, 1–6. https://doi.org/10.1109/ANDESCON50619.2020.9272177
Behera, S. K., Rath, A. K., & Sethy, P. K. (2020). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture. https://doi.org/https://doi.org/10.1016/j.inpa.2020.05.003
Bishop, C. M. (2006). Pattern Recognition and Machine Learning (1st ed. 20). Retrieved from http://gen.lib.rus.ec/book/index.php?md5=6b552b24cae380bb656f7aaef7f81b46
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851
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Forsyth, D., & Ponce, J. (2012). Computer Vision: A Modern Approach. Retrieved from https://books.google.com.co/books?id=gM63QQAACAAJ
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Nanaa, K., Rizon, M., Rahman, M. N. A., Ibrahim, Y., & Aziz, A. Z. A. (2014). Detecting Mango Fruits by Using Randomized Hough Transform and Backpropagation Neural Network. 2014 18th International Conference on Information Visualisation, 388–391. https://doi.org/10.1109/IV.2014.54
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spelling Pardo, CamiloÁvila, AdolfoBautista Gordo, Cristian JavierFonseca Cala, Yesid AldemarUniversidad Santo Tomás seccional Tunja2021-04-23T22:47:35Z2021-04-23T22:47:35Z2021-04-22Bautista, C., & Fonseca, Y. (2021). Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia Artificial.http://hdl.handle.net/11634/33770reponame:Repositorio Institucional Universidad Santo Tomásinstname:Universidad Santo Tomásrepourl:https://repository.usta.edu.coEn el desarrollo de esta investigación, se implementa un prototipo de banda transportadora para la selección de ciruela, en la cual se instala una sección de sensado compuesta de una cámara contenida en un ambiente de luminosidad controlada. Las imágenes adquiridas por el sistema de sensado, fueron sometidas, por una parte; a algoritmos de Visión por Computador y Deep Learning, orientados a la extracción de características y, por otra parte; a algoritmos de Machine Learning y Deep Learning, orientados a la clasificación del fruto en tres categorías, definidas por sus características morfológicas y deficiencias nutricionales (afectaciones visuales). Las primeras pruebas para llegar a una clasificación satisfactoria, se realizaron aplicando sobre las imágenes múltiples técnicas de Visión por Computador como: Detección de bordes de Canny, operaciones morfológicas, segmentación de fondo por umbrales, entre otras técnicas que permiten hacer ingeniería de características, las cuales dieron paso a una estructura de clasificación condicional. Posteriormente, se hicieron pruebas con árboles de decisión, máquinas de soporte vectorial, perceptrón multicapa y K-Vecino más cercano (KNN). Finalmente, se implementó una estructura de red neuronal convolucional (CNN).The development of this research, a prototype of a conveyor belt for the selection of plums is implemented, in which a sensing section composed of a camera contained in an environment of controlled luminosity is installed. The images acquired by the sensing system were submitted, on the one hand to Computer Vision and Deep Learning algorithms, oriented to the extraction of characteristics and, on the other hand to Machine Learning and Deep Learning algorithms, aimed at classifying the fruit into three categories, defined by their morphological characteristics and nutritional deficiencies (visual impairments). The first tests to reach a satisfactory classification were carried out by applying multiple Computer Vision techniques to the images, such as: Canny edge detection, morphological operations, background segmentation by thresholds, among other techniques that allow engineering characteristics, the which gave way to a conditional classification structure. Subsequently, tests were made with decision trees, vector support machines, multilayer perceptron and K-Nearest Neighbor (KNN). Finally, a convolutional neural network (CNN) structure was implemented.Ingeniero ElectronicoPregradoapplication/pdfspaUniversidad Santo TomásPregrado Ingeniería ElectrónicaFacultad de Ingeniería ElectrónicaAtribución-NoComercial-SinDerivadas 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sistema Clasificador de Ciruela Horvin Usando Técnicas de Inteligencia ArtificialArtificial IntelligencePlum SelectionImage ClassificationComputer VisionMachine LearningInteligencia ArtificialSelección de CiruelaClasificación de ImágenesVisión por computadorAprendizaje automáticoTrabajo de gradoinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesisCRAI-USTA Tunja1.17. Neural network models (supervised) — scikit-learn 0.24.1 documentation. (n.d.). Retrieved March 6, 2021, from https://scikit-learn.org/stable/modules/neural_networks_supervised.html#multi-layer-perceptronAggarwal, C. C. (2018). Neural Networks and Deep Learning. In Neural Networks and Deep Learning. https://doi.org/10.1007/978-3-319-94463-0Alegre, E. G., Pajares, M., & de la Escalera Hueso, A. (2016). Conceptos y métodos en visión por computador. Retrieved from https://books.google.com.co/books?id=9g9UAQAACAAJBautista, C. J., Aldemar Fonseca, Y., & Pardo-Beainy, C. (2020). Plum selection system using computer vision. 2020 IEEE ANDESCON, 1–6. https://doi.org/10.1109/ANDESCON50619.2020.9272177Behera, S. K., Rath, A. K., & Sethy, P. K. (2020). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture. https://doi.org/https://doi.org/10.1016/j.inpa.2020.05.003Bishop, C. M. (2006). Pattern Recognition and Machine Learning (1st ed. 20). Retrieved from http://gen.lib.rus.ec/book/index.php?md5=6b552b24cae380bb656f7aaef7f81b46Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851Chollet, F. (2017). Deep Learning with Python (1st ed.). USA: Manning Publications Co.CUDA GPUs | NVIDIA Developer. (n.d.). Retrieved March 9, 2021, from https://developer.nvidia.com/cuda-gpusDávila-Rodríguez, I., Nuño-Maganda, M., Hernández-Mier, Y., & Polanco-Martagón, S. (2019). Decision-Tree Based Pixel Classification for Real-time Citrus Segmentation on FPGA. 2019 International Conference on ReConFigurable Computing and FPGAs (ReConFig), 1–8. https://doi.org/10.1109/ReConFig48160.2019.8994792Forsyth, D., & Ponce, J. (2012). Computer Vision: A Modern Approach. Retrieved from https://books.google.com.co/books?id=gM63QQAACAAJGarcia-Lamont, F., Cervantes, J., López, A., & Rodriguez, L. (2018). 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