| dc.contributor.author | Heredia Gómez, Juan F. | |
| dc.contributor.author | Rueda Gómez, Juan P. | |
| dc.contributor.author | Talero Sarmiento, Leonardo H. | |
| dc.contributor.author | Ramírez Acuña, Juan S. | |
| dc.contributor.author | Coronado Silva, Roberto A. | |
| dc.date.accessioned | 2024-09-06T15:04:13Z | |
| dc.date.available | 2024-09-06T15:04:13Z | |
| dc.date.issued | 2020-10-19 | |
| dc.identifier.issn | ISSN: 1657-2831 | spa |
| dc.identifier.issn | e-ISSN: 2539-2115 | spa |
| dc.identifier.uri | http://hdl.handle.net/20.500.12749/26394 | |
| dc.description.abstract | Una 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. | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.relation | https://revistas.unab.edu.co/index.php/rcc/article/view/4030/3341 | spa |
| dc.relation.uri | https://revistas.unab.edu.co/index.php/rcc/issue/view/267 | spa |
| dc.source | Vol. 21 Núm. 2 (2020): Revista Colombiana de Computación (Julio-Diciembre); 42-55 | spa |
| dc.subject | Cacao | spa |
| dc.subject | Clasificación de Imágenes | spa |
| dc.subject | Detección de objetos | spa |
| dc.subject | Madurez | spa |
| dc.subject | Reconocimiento de Imágenes | spa |
| dc.subject | YOLO | spa |
| dc.subject | Raspberry Pi | spa |
| dc.title | Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido | spa |
| dc.title.translated | Cocoa pods ripeness estimation, using convolutional neural networks in an embedded system | eng |
| dc.type.driver | info:eu-repo/semantics/article | |
| dc.type.local | Artículo | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
| dc.subject.keywords | Cocoa | eng |
| dc.subject.keywords | Image classification | eng |
| dc.subject.keywords | Object detection | eng |
| dc.subject.keywords | Image classification | eng |
| dc.subject.keywords | YOLO | eng |
| dc.subject.keywords | Ripeness | eng |
| dc.subject.keywords | Raspberry pi | eng |
| dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.type.hasversion | info:eu-repo/semantics/publishedVersion | |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
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| dc.identifier.repourl | repourl:https://repository.unab.edu.co | spa |
| dc.description.abstractenglish | A correct cocoa harvest involves determining a pod maturity. However, this farm activity is usually handmade, using criteria such as Size and Color of the pod; those characteristics differ according to the cocoa variety, making it difficult to standardize. For this reason, this work proposes an automated method to simplify the number of variables to develop a portable, low-cost, and custom-made tool, which makes use of a convolutional neural network to indicate whether a cocoa pod is found it at the right time to harvest. The main results of this work are: 1) the construction of three labeled data sets (1992 images each), and 2) we developed an embedded system with a 34.83% mAP (mean Average Precision) accuracy. Finally, variance analysis demonstrates that image size (i.e., 4033x4033 p, 1009x1009 p, and 505x505 p) does not affect accuracy. | eng |
| dc.identifier.doi | https://doi.org/10.29375/25392115.4030 | |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
