• Repositorio Institucional Universidad de Pamplona
  • Tesis de maestría y doctorado
  • Facultad de Ingenierías y Arquitectura
  • Maestría en Controles Industriales
    • Por favor, use este identificador para citar o enlazar este ítem: http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3330
    Registro completo de metadatos
    Campo DC Valor Lengua/Idioma
    dc.contributor.authorPelaez Carrillo, Diego Alfonso.-
    dc.date.accessioned2022-10-03T14:27:12Z-
    dc.date.available2019-10-19-
    dc.date.available2022-10-03T14:27:12Z-
    dc.date.issued2020-
    dc.identifier.citationPelaez Carrillo, D. A. (2019). Caracterización de suelos con potencial productivo en el departamento de Norte de Santander usando cámara multiespectral en un vehículo aéreo no tripulado [Trabajo de Grado Maestría, Universidad de Pamplona]. Repositorio Hulago Universidad de Pamplona. http://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3330es_CO
    dc.identifier.urihttp://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/3330-
    dc.descriptionEl autor no proporciona la información sobre este ítem.es_CO
    dc.description.abstractEl autor no proporciona la información sobre este ítem.es_CO
    dc.format.extent104es_CO
    dc.format.mimetypeapplication/pdfes_CO
    dc.language.isoeses_CO
    dc.publisherUniversidad de Pamplona – Facultad de Ingenierías y Arquitectura.es_CO
    dc.subjectEl autor no proporciona la información sobre este ítem.es_CO
    dc.titleCaracterización de suelos con potencial productivo en el departamento de Norte de Santander usando cámara multiespectral en un vehículo aéreo no tripulado.es_CO
    dc.typehttp://purl.org/coar/resource_type/c_bdcces_CO
    dc.date.accepted2019-07-19-
    dc.relation.referencesAdhiwibawa, M. A. S., Setiawan, Y. E., Setiawan, Y., Prilianti, K. R., & Brotosudarmo, T. H. P. (2015). Application of Simple Multispectral Image Sensor and Artificial Intelligence for Predicting of Drought Tolerant Variety of Soybean. Procedia Chemistry, 14, 246–255. https://doi.org/https://doi.org/10.1016/j.proche.2015.03.035es_CO
    dc.relation.referencesAja, S. T. (2017). Análisis de imágenes multiespectrales aéreas de vegetación. Universidad de Cantabria.es_CO
    dc.relation.referencesAnthony, D., Elbaum, S., & Lorenz, A. (2014). On crop height estimation with UAVs. Intelligent Robots and Systems (IROS).es_CO
    dc.relation.referencesBrandao, A., Martins, F., & Soneguetti, H. (2015). A vision-based Line Following Strategy for an Autonomous UAV. Informatics in Control, Automation and Robotics (ICINCO).es_CO
    dc.relation.referencesBourgeon, M.-A., Paoli, J.-N., Jones, G., Villette, S., & Gée, C. (2016). Field radiometric calibration of a multispectral on-the-go sensor dedicated to the characterization of vineyard foliage. Computers and Electronics in Agriculture, 123, 184–194. https://doi.org/https://doi.org/10.1016/j.compag.2016.02.019es_CO
    dc.relation.referencesBurud, I., Lange, G., Lillemo, M., Bleken, E., Grimstad, L., & From, P. J. (2017). Exploring Robots and UAVs as Phenotyping Tools in Plant Breeding. IFAC PapersOnLine, 50(1), 11479–11484. https://doi.org/https://doi.org/10.1016/j.ifacol.2017.08.1591es_CO
    dc.relation.referencesCalvini, R., Amigo, J. M., & Ulrici, A. (2017). Transferring results from NIR hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee. Analytica Chimica Acta, 967, 33–41. https://doi.org/https://doi.org/10.1016/j.aca.2017.03.011es_CO
    dc.relation.referencesCao, S., Danielson, B., Clare, S., Koenig, S., Campos-Vargas, C., & Sanchez Azofeifa, A. (2019). Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols. ISPRS Journal of Photogrammetry and Remote Sensing, 149, 132–145. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2019.01.016es_CO
    dc.relation.referencesCeroni, M., Achkar, M., Inés, G., & Burgeño, J. (2015). Estudio del NDVI mediante análisis multiescalar y series temporales utilizando imágenes SPOT, durante el período 1998-2012 en el Uruguay. Revista de Teledetección, 31. https://doi.org/10.4995/raet.2015.3683es_CO
    dc.relation.referencesCui, Z., Wang, Y., Gao, X., Li, J., & Zheng, Y. (2016). Multispectral image classification based on improved weighted MRF Bayesian. Neurocomputing, 212, 75–87. https://doi.org/https://doi.org/10.1016/j.neucom.2016.03.097es_CO
    dc.relation.referencesChen, M., Zhou, J., Tao, G., Yang, J., & Hu, L. (2018). Wearable Affective Robot. IEEE Access, 6, 64766–64776. https://doi.org/10.1109/ACCESS.2018.2877919es_CO
    dc.relation.referencesDeng, L., Mao, Z., Li, X., Hu, Z., Duan, F., & Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 124– 136. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2018.09.008 comercio, S. d. (2015). vehiculos aereos no tripulados, drones. Boletin tecnologico.es_CO
    dc.relation.referencesDoering, D., Vizzotto, M. R., Bredemeier, C., da Costa, C. M., Henriques, R. V. B., Pignaton, E., & Pereira, C. E. (2016). MDE-based Development of a Multispectral Camera for Precision Agriculture. IFAC-PapersOnLine, 49(30), 24–29. https://doi.org/https://doi.org/10.1016/j.ifacol.2016.11.117es_CO
    dc.relation.referencesGiron Amaya, E., & Mahecha Anzola, X. (2015). Analisis descriptivo de la evolución de la agroindustria de la palma de aceite en Colombia a partir de los censos palmeros 1997 y 2011. Palmas, 13-25.es_CO
    dc.relation.referencesGongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2015). Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture, 116, 8–19. https://doi.org/https://doi.org/10.1016/j.compag.2015.05.021es_CO
    dc.relation.referencesKhodabakhshian, R., Emadi, B., Khojastehpour, M., & Golzarian, M. R. (2017). Determining quality and maturity of pomegranates using multispectral imaging. Journal of the Saudi Society of Agricultural Sciences, 16(4), 322–331. https://doi.org/https://doi.org/10.1016/j.jssas.2015.10.004es_CO
    dc.relation.referencesKleefeld, A., Gypser, S., Herppich, W. B., Bader, G., & Veste, M. (2018). Identification of spatial pattern of photosynthesis hotspots in moss- and lichen dominated biological soil crusts by combining chlorophyll fluorescence imaging and multispectral BNDVI images. Pedobiologia, 68, 1–11. https://doi.org/https://doi.org/10.1016/j.pedobi.2018.04.001es_CO
    dc.relation.referencesLiu, S., Li, L., Gao, W., Zhang, Y., Liu, Y., Wang, S., & Lu, J. (2018). Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. Computers and Electronics in Agriculture, 151, 185–195. https://doi.org/https://doi.org/10.1016/j.compag.2018.05.026es_CO
    dc.relation.referencesLiu, C., Hao, G., Su, M., Chen, Y., & Zheng, L. (2017). Potential of multispectral imaging combined with chemometric methods for rapid detection of sucrose adulteration in tomato paste. Journal of Food Engineering, 215, 78–83. https://doi.org/https://doi.org/10.1016/j.jfoodeng.2017.07.026es_CO
    dc.relation.referencesLiu, H., & Chahl, J. S. (2018). A multispectral machine vision system for invertebrate detection on green leaves. Computers and Electronics in Agriculture, 150, 279– 288. https://doi.org/https://doi.org/10.1016/j.compag.2018.05.002es_CO
    dc.relation.referencesLi, Y., Lu, H., Nakayama, Y., Kim, H., & Serikawa, S. (2018). Automatic road detection system for an air–land amphibious car drone. Future Generation Computer Systems, 85, 51–59. https://doi.org/https://doi.org/10.1016/j.future.2018.02.036es_CO
    dc.relation.referencesLogofătu, P. C., & Damian, V. (2019). Snapshot interferometric multispectral imaging using deconvolution and colorimetric fit. Optics & Laser Technology, 111, 100– 109. https://doi.org/https://doi.org/10.1016/j.optlastec.2018.09.008es_CO
    dc.relation.referencesLuhmann, T., Fraser, C., & Maas, H.-G. (2016). Sensor modelling and camera calibration for close-range photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 37–46. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2015.10.006es_CO
    dc.relation.referencesNandibewoor, A., Hebbal, S. B., & Hegadi, R. (2015). Remote Monitoring of Maize Crop through Satellite Multispectral Imagery. Procedia Computer Science, 45, 344–353. https://doi.org/https://doi.org/10.1016/j.procs.2015.03.158 https://doi.org/https://doi.org/10.1016/j.heliyon.2019.e01277es_CO
    dc.relation.referencesSaura, J. R., Reyes-Menendez, A., & Palos-Sanchez, P. (2019). Mapping multispectral Digital Images using a Cloud Computing software: applications from UAV images. Heliyon, 5(2), e01277.es_CO
    dc.relation.referencesSun, B., Yuan, N., Cao, C., & Hardeberg, J. Y. (2018). Design of four-band multispectral imaging system with one single-sensor. Future Generation Computer Systems, 86, 670–679. https://doi.org/https://doi.org/10.1016/j.future.2018.04.056es_CO
    dc.relation.referencesVerrelst, J., Rivera, J. P., Gitelson, A., Delegido, J., Moreno, J., & Camps-Valls, G. (2016). Spectral band selection for vegetation properties retrieval using Gaussian processes regression. International Journal of Applied Earth Observation and Geoinformation, 52, 554–567. https://doi.org/https://doi.org/10.1016/j.jag.2016.07.016es_CO
    dc.relation.referencesYu, R., Kang, J., Huang, X., Xie, S., Zhang, Y., & Gjessing, S. (2016). MixGroup: Accumulative Pseudonym Exchanging for Location Privacy Enhancement in Vehicular Social Networks. IEEE Transactions on Dependable and Secure Computing, 13(1), 93–105. https://doi.org/10.1109/TDSC.2015.2399291es_CO
    dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2es_CO
    dc.type.coarversionhttp://purl.org/coar/resource_type/c_2df8fbb1es_CO
    Aparece en las colecciones: Maestría en Controles Industriales

    Ficheros en este ítem:
    Fichero Descripción Tamaño Formato  
    Pelaez_2019_TG.pdfPelaez_2019_TG.pdf6,05 MBAdobe PDFVisualizar/Abrir
    Mostrar el registro sencillo del ítem


    Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.