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| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Patiño Peñaloza, Jhonny Alfonso | - |
| dc.date.accessioned | 2026-10-16T02:01:10Z | - |
| dc.date.available | 2024 | - |
| dc.date.available | 2026-10-16T02:01:10Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | Patiño Peñaloza, J. A. (2024). Diseño e implementación de un algoritmo basado en aprendizaje profundo para la extracción de propiedades y atributos discriminantes de frutos cítricos desde imágenes espectrales tipo vnir. [Trabajo de Grado Pregrado, Universidad de Pamplona]. Repositorio Hulago Universidad de Pamplona. https://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/11795 | es_CO |
| dc.identifier.uri | https://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/11795 | - |
| dc.description | Este trabajo presenta el desarrollo e implementación de un sistema basado en imágenes hiperespectrales en el rango visible e infrarrojo cercano (VNIR) y redes neuronales profundas, diseñado para clasificar y evaluar el estado de maduración de frutos cítricos, específicamente mandarinas. Utilizando una cámara hiperespectral Specim FX10e, operando entre 400 y 1000 nm, se capturaron datacubos hiperespectrales que permitieron analizar propiedades internas y externas de los frutos. Las imágenes fueron procesadas mediante técnicas de reducción de dimensionalidad, como el Análisis de Componentes Principales (PCA), para extraer las bandas espectrales más relevantes, preservando el 97% de la información útil en las firmas espectrales. Para la clasificación, se desarrolló una red neuronal profunda utilizando el algoritmo Levenberg-Marquardt backpropagation, configurada para optimizar la identificación de estados de maduración (verde, pintón y maduro). Este enfoque permitió alcanzar una precisión del 100% en la matriz de confusión, evidenciando la eficacia del sistema en condiciones controladas. La segmentación y selección de bandas específicas (562.4 nm, 750 nm y 951.7 nm) fueron claves para eliminar el ruido y mejorar la discriminación de clases. Más allá de la aplicación práctica en la clasificación de cítricos, este estudio contribuye significativamente al campo de la espectroscopía hiperespectral y el procesamiento de imágenes al integrar técnicas de aprendizaje profundo y reducción de dimensionalidad con datos espectrales complejos. Los resultados obtenidos demuestran que el análisis hiperespectral combinado con redes neuronales puede superar las limitaciones de métodos tradicionales, proporcionando una solución no destructiva, precisa y escalable para la evaluación de calidad en productos agrícolas. Este trabajo sienta las bases para futuras investigaciones en el desarrollo de sistemas de clasificación automatizada, no solo en frutos cítricos, sino también en otros productos agrícolas. Además, ofrece un enfoque metodológico robusto que puede ser adaptado a diferentes entornos industriales, impulsando la modernización del control de calidad en la agroindustria. | es_CO |
| dc.description.abstract | This study presents the development and implementation of a system based on hyperspectral imaging in the visible and near-infrared range (VNIR) and deep neural networks, designed to classify and evaluate the ripening stages of citrus fruits, specifically mandarins. Using a Specim FX10e hyperspectral camera operating between 400 and 1000 nm, hyperspectral datacubes were captured to analyze both internal and external properties of the fruits. The images were processed using dimensionality reduction techniques, such as Principal Component Analysis (PCA), to extract the most relevant spectral bands, retaining 97% of the useful information in the spectral signatures. For classification, a deep neural network was developed using the Levenberg-Marquardt backpropagation algorithm, configured to optimize the identification of ripening stages (green, partially ripe, and fully ripe). This approach achieved 100% precision in the confusion matrix, demonstrating the system’s effectiveness under controlled conditions. Segmentation and selection of specific bands (562.4 nm, 750 nm, and 951.7 nm) were key to noise reduction and improved class discrimination. Beyond its practical application in citrus fruit classification, this study significantly contributes to the field of hyperspectral spectroscopy and image processing by integrating deep learning and dimensionality reduction techniques with complex spectral data. The results demonstrate that hyperspectral analysis combined with neural networks can overcome the limitations of traditional methods, providing a non-destructive, precise, and scalable solution for quality assessment in agricultural products. This work lays the foundation for future research in automated classification systems, not only for citrus fruits but also for other agricultural products. Additionally, it offers a robust methodological framework that can be adapted to various industrial environments, driving the modernization of quality control in the agroindustry. | es_CO |
| dc.format.extent | 81 | es_CO |
| dc.format.mimetype | application/pdf | es_CO |
| dc.language.iso | es | es_CO |
| dc.publisher | Universidad de Pamplona - Facultad de Ciencias Básicas. | es_CO |
| dc.subject | El autor no proporciona la información sobre este ítem. | es_CO |
| dc.title | Diseño e implementación de un algoritmo basado en aprendizaje profundo para la extracción de propiedades y atributos discriminantes de frutos cítricos desde imágenes espectrales tipo VNIR. | es_CO |
| dc.type | http://purl.org/coar/resource_type/c_7a1f | es_CO |
| dc.date.accepted | 2024 | - |
| dc.description.edition | Universidad de Pamplona - Facultad de Ciencias Básicas. | es_CO |
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| dc.rights.accessrights | http://purl.org/coar/access_right/c_abf2 | es_CO |
| dc.type.coarversion | http://purl.org/coar/resource_type/c_2df8fbb1 | es_CO |
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