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Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido

Cocoa pods ripeness estimation, using convolutional neural networks in an embedded system

dc.creatorHeredia Gómez, Juan F.
dc.creatorRueda Gómez, Juan P.
dc.creatorTalero Sarmiento, Leonardo H.
dc.creatorRamírez Acuña, Juan S.
dc.creatorCoronado Silva, Roberto Antonio
dc.date2024-08-05T17:31:23Z
dc.date2024-08-05T17:31:23Z
dc.date2020-12-01
dc.date2020
dc.date.accessioned2024-11-14T14:59:58Z
dc.date.available2024-11-14T14:59:58Z
dc.identifierhttps://revistas.unab.edu.co/index.php/rcc/article/view/4030
dc.identifier2539-2115
dc.identifierhttp://hdl.handle.net/20.500.12324/39787
dc.identifierhttps://doi.org/10.29375/25392115.4030
dc.identifierreponame:Biblioteca Digital Agropecuaria de Colombia
dc.identifierinstname:Corporación colombiana de investigación agropecuaria AGROSAVIA
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/85171
dc.descriptionUna correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que difieren según la variedad cultivada, lo cual dificulta su estandarización. Con el fin de simplificar la cantidad de variables y presentar un método automatizado, el presente trabajo propone desarrollar una herramienta portable, de bajo costo, y hecha a medida, la cual hace uso de una red neuronal convolucional para indicar si una mazorca de cacao se encuentra en el momento oportuno para ser cosechada. Entre los principales resultados del presente trabajo se encuentran: 1) la construcción de tres conjuntos de datos etiquetados (1992 imágenes cada uno), y 2) un sistema embebido con una precisión de 34.83% mAP (mean Average Precision). Finalmente, se demuestra estadísticamente que el tamaño de las imágenes (4033x4033 p, 1009x1009 p y 505x505 p) no incide sobre la eficacia del entrenamiento.
dc.descriptionCacao-Theobroma cacao
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dc.languagespa
dc.publisherUniversidad Autónoma de Bucaramanga
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dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceRev. colomb. comput.; vol. 21, Núm. 2,(2020): Rev. colomb. comput.(Dic.);p. 42–55.
dc.subjectCacao
dc.subjectClasificación de Imágenes
dc.subjectDetección de objetos
dc.subjectMadurez
dc.subjectReconocimiento de Imágenes
dc.subjectYOLO
dc.subjectRaspberry Pi
dc.subjectProducción y tratamiento de semillas - F03
dc.subjectTheobroma cacao
dc.subjectMadurez
dc.subjectImagen
dc.subjectCacao
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_7713
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_4656
dc.subjecthttp://aims.fao.org/aos/agrovoc/c_36760
dc.titleDeterminación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido
dc.titleCocoa pods ripeness estimation, using convolutional neural networks in an embedded system
dc.typeArtículo científico
dc.coverageColombia
dc.audienceInvestigador
dc.thumbnailhttps://repository.agrosavia.co/bitstream/20.500.12324/39787/4/Ver_Documento_39787.pdf.jpg


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