Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales
his work focuses on solving the problem of classification and detection of black Sigatoka disease in banana plants, in terms of improving the process used in Colombia and reducing costs for the disease control process, based on the use of neural networks with the VGG19 Architecture. An automated too...
- Autores:
-
Pallares, Carlos Jorge
Lallemand, Keneth Stive
Visbal, Fernando David
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad del Norte
- Repositorio:
- Repositorio Uninorte
- Idioma:
- spa
- OAI Identifier:
- oai:manglar.uninorte.edu.co:10584/9533
- Acceso en línea:
- http://hdl.handle.net/10584/9533
- Palabra clave:
- Machine learning
Fourè
Segmentación
redes neuronales convolucionales
Sigatoka negra
enfermedad
foliar
preventivo
fitosanitario
agricultura de precisión
reducción de costos
- Rights
- License
- Universidad del Norte
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oai:manglar.uninorte.edu.co:10584/9533 |
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REPOUNORT2 |
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Repositorio Uninorte |
repository_id_str |
|
dc.title.es_ES.fl_str_mv |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
title |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
spellingShingle |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales Machine learning Fourè Segmentación redes neuronales convolucionales Sigatoka negra enfermedad foliar preventivo fitosanitario agricultura de precisión reducción de costos |
title_short |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
title_full |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
title_fullStr |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
title_full_unstemmed |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
title_sort |
Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales |
dc.creator.fl_str_mv |
Pallares, Carlos Jorge Lallemand, Keneth Stive Visbal, Fernando David |
dc.contributor.advisor.none.fl_str_mv |
Zurek, Eduardo |
dc.contributor.author.none.fl_str_mv |
Pallares, Carlos Jorge Lallemand, Keneth Stive Visbal, Fernando David |
dc.subject.es_ES.fl_str_mv |
Machine learning Fourè Segmentación redes neuronales convolucionales Sigatoka negra enfermedad foliar preventivo fitosanitario agricultura de precisión reducción de costos |
topic |
Machine learning Fourè Segmentación redes neuronales convolucionales Sigatoka negra enfermedad foliar preventivo fitosanitario agricultura de precisión reducción de costos |
description |
his work focuses on solving the problem of classification and detection of black Sigatoka disease in banana plants, in terms of improving the process used in Colombia and reducing costs for the disease control process, based on the use of neural networks with the VGG19 Architecture. An automated tool is proposed for analysis, control and monitoring of Black Sigatoka. The analysis, control and monitoring will be done thanks to the reports as the final result of our tool, which will seek to be as explicit as possible for the end user in terms of location, severity and visualization of results classified in fields. The development of this project will base the use of tools for automation of processes in agriculture in Magdalena as it is based on real data and current deep learning techniques, exposing a vision of the use of precision agriculture as a set of techniques where the technology will begin to base decisions for the improvement of crops in terms of control, analysis and phytosanitary monitoring of diseases and associated fungi. |
publishDate |
2021 |
dc.date.accessioned.none.fl_str_mv |
2021-05-31T13:18:00Z |
dc.date.available.none.fl_str_mv |
2021-05-31T13:18:00Z |
dc.date.issued.none.fl_str_mv |
2021-05-28 |
dc.type.es_ES.fl_str_mv |
article |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10584/9533 |
url |
http://hdl.handle.net/10584/9533 |
dc.language.iso.es_ES.fl_str_mv |
spa |
language |
spa |
dc.rights.es_ES.fl_str_mv |
Universidad del Norte |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Universidad del Norte http://purl.org/coar/access_right/c_abf2 |
dc.publisher.es_ES.fl_str_mv |
Barranquilla, Universidad del Norte, 2021 |
institution |
Universidad del Norte |
bitstream.url.fl_str_mv |
https://manglar.uninorte.edu.co/bitstream/10584/9533/1/Paper%20Proyecto%20de%20Grado.docx%20%287%29.pdf https://manglar.uninorte.edu.co/bitstream/10584/9533/2/license.txt |
bitstream.checksum.fl_str_mv |
622baebb9838c49a2bd63b079f5c9482 8a4605be74aa9ea9d79846c1fba20a33 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Digital de la Universidad del Norte |
repository.mail.fl_str_mv |
mauribe@uninorte.edu.co |
_version_ |
1812183098824065024 |
spelling |
Zurek, EduardoPallares, Carlos JorgeLallemand, Keneth StiveVisbal, Fernando David2021-05-31T13:18:00Z2021-05-31T13:18:00Z2021-05-28http://hdl.handle.net/10584/9533his work focuses on solving the problem of classification and detection of black Sigatoka disease in banana plants, in terms of improving the process used in Colombia and reducing costs for the disease control process, based on the use of neural networks with the VGG19 Architecture. An automated tool is proposed for analysis, control and monitoring of Black Sigatoka. The analysis, control and monitoring will be done thanks to the reports as the final result of our tool, which will seek to be as explicit as possible for the end user in terms of location, severity and visualization of results classified in fields. The development of this project will base the use of tools for automation of processes in agriculture in Magdalena as it is based on real data and current deep learning techniques, exposing a vision of the use of precision agriculture as a set of techniques where the technology will begin to base decisions for the improvement of crops in terms of control, analysis and phytosanitary monitoring of diseases and associated fungi.spaBarranquilla, Universidad del Norte, 2021Universidad del Nortehttp://purl.org/coar/access_right/c_abf2Machine learningFourèSegmentaciónredes neuronales convolucionalesSigatoka negraenfermedadfoliarpreventivofitosanitarioagricultura de precisiónreducción de costosControl preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionalesarticlehttp://purl.org/coar/resource_type/c_6501ORIGINALPaper Proyecto de Grado.docx (7).pdfPaper Proyecto de Grado.docx (7).pdfapplication/pdf2667797https://manglar.uninorte.edu.co/bitstream/10584/9533/1/Paper%20Proyecto%20de%20Grado.docx%20%287%29.pdf622baebb9838c49a2bd63b079f5c9482MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://manglar.uninorte.edu.co/bitstream/10584/9533/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5210584/9533oai:manglar.uninorte.edu.co:10584/95332021-05-31 08:18:00.576Repositorio Digital de la Universidad del Nortemauribe@uninorte.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |