Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning
The use of new technologies for the recognition of fruits such as pineapple from image analysis is a current solution to the process of counting fruits that may be within reach of these new forms, for this it was possible to carry out this process using the network artificial neural (ANN), support v...
- Autores:
-
Riveros Parrado, Julian Mauricio
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad Antonio Nariño
- Repositorio:
- Repositorio UAN
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uan.edu.co:123456789/6919
- Acceso en línea:
- http://repositorio.uan.edu.co/handle/123456789/6919
- Palabra clave:
- Piña
RPA
programación
conteo
Pineapple
RPA
programming
counting
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| title |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| spellingShingle |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning Piña RPA programación conteo Pineapple RPA programming counting |
| title_short |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| title_full |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| title_fullStr |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| title_full_unstemmed |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| title_sort |
Identificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine Learning |
| dc.creator.fl_str_mv |
Riveros Parrado, Julian Mauricio |
| dc.contributor.advisor.spa.fl_str_mv |
Cucaita Gómez, Alexander |
| dc.contributor.author.spa.fl_str_mv |
Riveros Parrado, Julian Mauricio |
| dc.subject.es_ES.fl_str_mv |
Piña RPA programación conteo |
| topic |
Piña RPA programación conteo Pineapple RPA programming counting |
| dc.subject.keyword.es_ES.fl_str_mv |
Pineapple RPA programming counting |
| description |
The use of new technologies for the recognition of fruits such as pineapple from image analysis is a current solution to the process of counting fruits that may be within reach of these new forms, for this it was possible to carry out this process using the network artificial neural (ANN), support vector machine (SVM), random forest (RF), Naive Bayes (NB), decision trees (DT) and k nearest neighbor (KNN). |
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2022 |
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2022-09-01T20:49:30Z |
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2022-09-01T20:49:30Z |
| dc.date.issued.spa.fl_str_mv |
2022-06-04 |
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Trabajo de grado (Pregrado y/o Especialización) |
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http://repositorio.uan.edu.co/handle/123456789/6919 |
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Al-Zebari, A., & Sengur, A. (2019). Performance comparison of machine learning techniques on diabetes disease detection. 2019 1st International Informatics and Software Engineering Conference (UBMYK), 1–4. Alzu’bi, R., Anushya, A., Hamed, E., Al Sha’ar, E. A., & Vincy, B. S. A. (2018). Dates fruits classification using SVM. AIP Conference Proceedings, 1952(1), 20078. Anitha, P., & Chakravarthy, T. (2018). Agricultural Crop Yield Prediction using Artificial Neural Network with Feed Forward Algorithm. International Journal of Computer Sciences and Engineering, 6(11), 178–181. https://doi.org/10.26438/ijcse/v6i11.178181 Arowolo, M. O., Abdulsalam, S. O., Saheed, Y. K., & Salawu, M. D. (2016). A feature selection based on one-way-ANOVA for microarray data classification. Al-Hikmah J Pure Appl Sci, 3, 30–35. Babikir, H. A., Elaziz, M. A., Elsheikh, A. H., Showaib, E. A., Elhadary, M., Wu, D., & Liu, Y. (2019). Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model. Alexandria Engineering Journal, 58(3), 1077–1087. https://doi.org/10.1016/j.aej.2019.09.010 Basso, M., & Pignaton de Freitas, E. (2020). A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent & Robotic Systems, 97(3), 605–621. https://doi.org/10.1007/s10846-019-01006-0 Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 403. Blok, P. M., Barth, R., & van den Berg, W. (2016). Machine vision for a selective broccoli harvesting robot. IFAC-PapersOnLine, 49(16), 66–71. https://doi.org/https://doi.org/10.1016/j.ifacol.2016.10.013 Calou, V. B. C., Teixeira, A. dos S., Moreira, L. C. J., Lima, C. S., de Oliveira, J. B., & de Oliveira, M. R. R. (2020). The use of UAVs in monitoring yellow sigatoka in banana. Biosystems Engineering, 193, 115–125. Dhalia Sweetlin, J., Nehemiah, H. K., & Kannan, A. (2018). Computer aided diagnosis of pulmonary hamartoma from CT scan images using ant colony optimization based |
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Al-Zebari, A., & Sengur, A. (2019). Performance comparison of machine learning techniques on diabetes disease detection. 2019 1st International Informatics and Software Engineering Conference (UBMYK), 1–4. Alzu’bi, R., Anushya, A., Hamed, E., Al Sha’ar, E. A., & Vincy, B. S. A. (2018). Dates fruits classification using SVM. AIP Conference Proceedings, 1952(1), 20078. Anitha, P., & Chakravarthy, T. (2018). Agricultural Crop Yield Prediction using Artificial Neural Network with Feed Forward Algorithm. International Journal of Computer Sciences and Engineering, 6(11), 178–181. https://doi.org/10.26438/ijcse/v6i11.178181 Arowolo, M. O., Abdulsalam, S. O., Saheed, Y. K., & Salawu, M. D. (2016). A feature selection based on one-way-ANOVA for microarray data classification. Al-Hikmah J Pure Appl Sci, 3, 30–35. Babikir, H. A., Elaziz, M. A., Elsheikh, A. H., Showaib, E. A., Elhadary, M., Wu, D., & Liu, Y. (2019). Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model. Alexandria Engineering Journal, 58(3), 1077–1087. https://doi.org/10.1016/j.aej.2019.09.010 Basso, M., & Pignaton de Freitas, E. (2020). A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent & Robotic Systems, 97(3), 605–621. https://doi.org/10.1007/s10846-019-01006-0 Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 403. Blok, P. M., Barth, R., & van den Berg, W. (2016). Machine vision for a selective broccoli harvesting robot. IFAC-PapersOnLine, 49(16), 66–71. https://doi.org/https://doi.org/10.1016/j.ifacol.2016.10.013 Calou, V. B. C., Teixeira, A. dos S., Moreira, L. C. J., Lima, C. S., de Oliveira, J. B., & de Oliveira, M. R. R. (2020). The use of UAVs in monitoring yellow sigatoka in banana. Biosystems Engineering, 193, 115–125. Dhalia Sweetlin, J., Nehemiah, H. K., & Kannan, A. (2018). Computer aided diagnosis of pulmonary hamartoma from CT scan images using ant colony optimization based instname:Universidad Antonio Nariño reponame:Repositorio Institucional UAN repourl:https://repositorio.uan.edu.co/ |
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Facultad de Ingeniería Mecánica, Electrónica y Biomédica |
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Villavicencio |
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International Journal of Computer Sciences and Engineering, 6(11), 178–181. https://doi.org/10.26438/ijcse/v6i11.178181Arowolo, M. O., Abdulsalam, S. O., Saheed, Y. K., & Salawu, M. D. (2016). A feature selection based on one-way-ANOVA for microarray data classification. Al-Hikmah J Pure Appl Sci, 3, 30–35.Babikir, H. A., Elaziz, M. A., Elsheikh, A. H., Showaib, E. A., Elhadary, M., Wu, D., & Liu, Y. (2019). Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model. Alexandria Engineering Journal, 58(3), 1077–1087. https://doi.org/10.1016/j.aej.2019.09.010Basso, M., & Pignaton de Freitas, E. (2020). A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms. Journal of Intelligent & Robotic Systems, 97(3), 605–621. https://doi.org/10.1007/s10846-019-01006-0Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. 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Computer aided diagnosis of pulmonary hamartoma from CT scan images using ant colony optimization basedinstname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/The use of new technologies for the recognition of fruits such as pineapple from image analysis is a current solution to the process of counting fruits that may be within reach of these new forms, for this it was possible to carry out this process using the network artificial neural (ANN), support vector machine (SVM), random forest (RF), Naive Bayes (NB), decision trees (DT) and k nearest neighbor (KNN).El uso de nuevas tecnologías para el reconocimiento de frutas como la piña a partir del analisis de imágenes, es una solucón actual al proceso de conteo de frutos que puede haber al alcance de estas nuevas formas, para ello se logró realizar este proceso usando la red neuronal artificial (ANN), máquina de vectores de soporte(SVM), bosque aleatorio (RF), Naive Bayes (NB), árboles de decisión (DT) y k vecino más cercanos (KNN).Ingeniero(a) Electromecánico(a)PregradoDistanciaProyectospaUniversidad Antonio NariñoIngeniería ElectromecánicaFacultad de Ingeniería Mecánica, Electrónica y BiomédicaVillavicencioPiñaRPAprogramaciónconteoPineappleRPAprogrammingcountingIdentificación automatizada de imágenes, detección y recuento de piña usando vista superior mediante Machine LearningTrabajo de grado (Pregrado y/o Especialización)http://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85EspecializadaORIGINAL2022_RiverosParradoJoséMauricio2022_RiverosParradoJoséMauricioREV. tURNITINapplication/pdf3862767https://repositorio.uan.edu.co/bitstreams/7b8fbe3b-d66c-48e8-b16e-24b811895f71/downloadb558daafbee4a9cd4c5120bb654bc6bbMD512022_RiverosParradoJoséMauricio2022_RiverosParradoJoséMauricioProyecto finalapplication/pdf3835139https://repositorio.uan.edu.co/bitstreams/a325ea27-cf88-444e-8d20-d34f6c72e749/download77aa0760c4c903244d31f4a739d89bf8MD522022_RiverosParradoJoséMauricio _Autorización2022_RiverosParradoJoséMauricio _AutorizaciónAutorización de autoresapplication/pdf1252562https://repositorio.uan.edu.co/bitstreams/0adc0be0-7b43-4bbb-a25c-00b1d1d83d64/download015311d00d3f540e05533862ee74e60bMD532022_RiverosParradoJoséMauricio _Acta2022_RiverosParradoJoséMauricio _ActaActa de sustentaciónapplication/pdf654059https://repositorio.uan.edu.co/bitstreams/e0dbb3ed-3612-4c2a-ad7d-e3d4fd8ccc64/downloadad264282ae5c5b44585c4051554d3d82MD54CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.uan.edu.co/bitstreams/b46dc550-563b-4600-9a03-90b4dc846645/download9868ccc48a14c8d591352b6eaf7f6239MD55123456789/6919oai:repositorio.uan.edu.co:123456789/69192024-10-09 23:17:25.518https://creativecommons.org/licenses/by-nc-nd/4.0/Acceso abiertoopen.accesshttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co |