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Drones Aplicados a la Agricultura de Precisión

dc.creatorGonzález, Adrián
dc.creatorAmarillo, Gelberth
dc.creatorAmarillo, Milton
dc.creatorSarmiento, Francisco
dc.date2016-03-22
dc.date.accessioned2019-11-08T21:23:48Z
dc.date.available2019-11-08T21:23:48Z
dc.identifierhttp://hemeroteca.unad.edu.co/index.php/publicaciones-e-investigacion/article/view/1585
dc.identifier10.22490/25394088.1585
dc.identifier.urihttps://repository.unad.edu.co/handle/10596/29774
dc.descriptionEl siguiente artículo presenta los drones como una tecnología que ayuda a los múltiples procesos de la agricultura, a captar información importante y a evaluar las condiciones de los terrenos monitoreados, gracias a sus grandes ventajas para sobrevolar los campos y los cultivos. Ahora no es completamente necesario recorrer todo el cultivo personalmente para detectar los problemas que sufre este, ya que con los drones el procedimiento de evaluar los cultivos se puede hacer de forma virtual, aplicando tecnologías de cámaras con alta definición e información georreferenciada para su ubicación exacta. Lo más importante es el poder determinar de forma prematura y eficiente las enfermedades, las plagas, la maleza y los posibles efectos futuros de daños climáticos como las heladas o sequías. La eficiencia, tanto ambiental como económica, ayuda en los procesos de siembra, costos de riego, abono y fumigación. es-ES
dc.formatapplication/pdf
dc.formattext/html
dc.languagespa
dc.publisherUniversidad Nacional Abierta y a Distancia, UNADes-ES
dc.relationhttp://hemeroteca.unad.edu.co/index.php/publicaciones-e-investigacion/article/view/1585/1917
dc.relationhttp://hemeroteca.unad.edu.co/index.php/publicaciones-e-investigacion/article/view/1585/1930
dc.relation/*ref*/L. Koh, S. Wich, “Dawn of drone ecology: low-cost autono-mous aerial vehicles for conservation”, 2012.
dc.relation/*ref*/J. McGlone, “Manual of Photogrammetry”, 2013.
dc.relation/*ref*/J. Paneque-Gálvez, M. McCall, B. Napoletano, S. Wich, L. Koh, “Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas”, 2014.
dc.relation/*ref*/P.J. Zarco-Tejada, M. Guillen, R. Hernandez, A. Catalina, M. Gonzalez, P. Martin, “Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)”, 2013.
dc.relation/*ref*/E. Plant, “Combining expert system and GIS technology to implement a state-transition model of oak woodlands”, 2000.
dc.relation/*ref*/M. Simard, N. Pinto, J. Fisher, A. Baccini, “Mapping forest canopy height globally with spaceborne lidar”, 2011.
dc.relation/*ref*/E. Næsset, H. Ørka, S. Solberg, O. Bollandsås, E. Hansen, E. Mauya, E. Zahabu, R. Malimbwi, N. Chamuya, H. Olsson, T. Gobakken, “Mapping and estimating forest area and above- ground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and glo- bal forest maps: A comparison of estimated precision”, Remote Sensing of Environment, Vol. 175, Marzo, 2016, pp. 282-300.
dc.relation/*ref*/R. Liu, R. Shang, Y. Liu, X. Lu, “Global evaluation of gap- filling approaches for seasonal NDVI with considering vege- tation growth trajectory, protection of key point, noise resis- tance and curve stability”, 2016.
dc.relation/*ref*/S. Roy, T. Rowlandson, A. Berg, Catherine Champagne, Jus- tin R. Adams, “Impact of sub-pixel heterogeneity on mode- lled brightness temperature for an agricultural region”, 2016.
dc.relation/*ref*/M. Elarab, A. Ticlavilca, A. Torres, I. Maslova, M. McKee, “Estimating chlorophyll with thermal and broadband multis- pectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agri- culture”, 2015.
dc.relation/*ref*/P. Mondal, M. Basu, “Adoption of precision agriculture tech- nologies in India and in some developing countries: Scope, present status and strategies”, Progress in Natural Science, Vol. 19, no. 6, pp.659-666, 2009.
dc.relation/*ref*/DAMS SRL. (2016). Agricultura. [Online]. Available: http://www.dams.com.ar/agricultura/#la-ciencia-detras-del-analisis T. Lillesand, R. Kiefer, J. Chipman., “Remote Sensing And Image Interpretation”, 2004.
dc.relation/*ref*/K. Mollazade, “Book review of “Imaging with electromagne- tic spectrum: Applications in food and agriculture””, Trends in Food Science & Technology, Vol. 50, 2016, pp. 262-263.
dc.relation/*ref*/R. Páscoa, M. Lopo, C. Teixeira , A. Graça, J. Lopes, “Explo- ratory study on vineyards soil mapping by visible/near-infra- red spectroscopy of grapevine leaves”, Computers and Elec- tronics in Agriculture, Vol. 127, Septiembre, 2016, pp 15-25.
dc.relation/*ref*/J. Díaz, “Estudio de índices de vegetación a partir de imá- genes aéreas tomadas desde Uas/Rpas y aplicaciones de estos a la agricultura de precisión. Madrid”, 2015.
dc.relation/*ref*/G. Rock, M. Gerhards, M. Schlerf, C. Hecker, T. Udelhoven, “Plant species discrimination using emissive thermal infrared imaging spectroscopy”, International Journal of Applied Earth Observation and Geoinformation, Vol. 53, 2016, pp. 16-26.
dc.relation/*ref*/B.Rao, A. Gopi, R. Maione, “The societal impact of commercial drones”, Technology in Society, Vol. 45, pp. 83-90, Mayo, 2016.
dc.relation/*ref*/K. Siva Balan, “Robotic-Based Agriculture for Rural Renais- sance: Drones and Biosensors”, Comprehensive Analytical Chemistry, Vol. 74, 2016, pp. 363-375.
dc.relation/*ref*/Drones Imaging. (2013). NDVI Cameras [Online]. Available: http://www.dronesimaging.com/en/solutions/ndvi-cameras/Airinov, (2016).
dc.relation/*ref*/Airinov multiSPEC 4C pour multirotors (anciennement AgroSensor). [Online]. Available: https://odoo. airinov.fr/shop/product/airinov-multispec-4c-pourmultiro- tors-anciennement-agrosensor-36
dc.relation/*ref*/S. Parrot, (2016). Parrot SEQUOIA. [Online]. Available: https://www.parrot.com/us/Business-solutions/parrot- sequoia#parrot-sequoia-
dc.relation/*ref*/Surface Optics Corporation. (2016). Agricultura de precisión y sensores hiperespectrales: Vigilancia contra la sequía, enfermedad y estrés de nutrientes. [Online]. Available: http:// www.precisiontech.com.ar/?p=215
dc.relation/*ref*/R. Moscetti, P. Haff, D. Monarca, M.Cecchini, R. Massantini, “Near-infrared spectroscopy for detection of hailstorm da- mage on olive fruit”, Postharvest Biology and Technology, Vol. 120, Octubre, 2016, pp. 204-212.
dc.relation/*ref*/M. Buitrago, T. Groen, C. Hecker, A. Skidmore, “Changes in thermal infrared spectra of plants caused by temperature and water stress”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 111, pp. 22-31.
dc.relation/*ref*/SenseFly. (2016). thermoMAP. [Online]. Available: https://www.sensefly.com/drones/accessories.html
dc.relation/*ref*/J. Ruiz. (2016). Sistemas de información geográfica y drones. [Online]. Available: http://www.ideyared.es/blog/2016/04/25/ sistemas-de-informacion-geografica-y-drones/
dc.relation/*ref*/N.An, C. Palmer, R. Baker, R. Markelz, M. Covington, J. Ma- loof, S. Welch, C.Weinig, “Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area”, Computers and Electronics in Agriculture, Vol. 127Septiembre, 2016, , pp. 376-394.
dc.relation/*ref*/INTA. (2014, Febrero 12). La Argentina consolida su lide- razgo en agricultura de precisión. [Online]. Available: http://intainforma.inta.gov.ar/?p=20305
dc.relation/*ref*/K. Ribeiro, D.Hernandez, R. Ballesteros, M. Moreno, “Appro- ximate georeferencing and automatic blurred image detec- tion to reduce the costs of UAV use in environmental and agricultural applications”, Biosystems Engineering, Vol. 151, Noviembre, 2016, pp. 308-327.
dc.relation/*ref*/SPIE. (2016). A new era in remote sensing of crops with unmanned robots. [Online]. Available: http://spie.org/ newsroom/1438-a-new-era-in-remote-sensing-of-crops- with-unmanned-robots
dc.relation/*ref*/Drone By Drone S.L. (2015, Octubre 14). Mapa de reflectan- cia del índice agrónomico NDVI en bodegas de la rioja. [On- line]. Available: http://www.dronebydrone.com/noticias/90/ mapa-de-reflectancia-del-ndice-agronomico-ndvi-en-bode- gas-de-la-rioja.html
dc.relation/*ref*/Hemav. (2016). Agricultura de precisión. [Online]. Available: http://hemav.com/servicio/agricultura-de-precision/
dc.relation/*ref*/S. Saleem, A. Bais, R. Sablatnig, “Towards feature points based image matching between satellite imagery and aerial photographs of agriculture land”, Computers and Electronics in Agriculture, Vol. 126, Agosto, 2016, pp. 12-20.
dc.relation/*ref*/C. Rokhmana. (2015, Mayo 22). Agricultura de precisión: del azadón al GPS. [Online]. Available: http://www.dinero.com/ especiales-comerciales/articulo/agricultura-precision-del- azadon-gps/208779.
dc.relation/*ref*/C. Rokhmana, “The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia”, Procedia Environmental Sciences, Vol. 24, 2015, pp. 245-253.
dc.relation/*ref*/L. Santesteban, S. Di Gennaro, A. Herrero, C. Miranda, J.B. Royo, A. Matese, “High-resolution UAV-based thermal ima- ging to estimate the instantaneous and seasonal variability of plant water status within a vineyard”, Agricultural Water Management, Septiembre, 2016.
dc.relation/*ref*/J. Lawton, D.E. Bignell, B. Bolton, G. Bloemers, P. Eggleton, P. Hammond, M. Hodda, R. Holt, T. Larsen, N. Mawdsley, N. Stork, D. Srivastava, A. Watt, “Biodiversity inventories, indicator taxa and effects of habitat modification in tropical forest”, 1998.
dc.relation/*ref*/T. Gardner, J. Barlow, I. Araujo, T. Avila-Pires, A. Bonaldo, J. Costa, M. Esposito, L. Ferreira, J. Hawes, M. Hernandez, M. Hoogmoed, R. Leite, N. Lo-Man-Hung, J. Malcolm, M. Martins, L. Mestre, R. Miranda-Santos, W. Overal, L. Pa- rry, S. Peters, M. Ribeiro, M. da Silva, C. Motta, C. Peres, “The cost-effectiveness of biodiversity surveys in tropical forests”, 2008.
dc.relation/*ref*/R. Condit, “Tropical Forest Census Plots Methods and Re- sults from Barro Colorado Island, Panama and a Compari- son with Other Plots”, 1998. Z. Huang, M. Meng, Y. Zhang, “Climate of Dinghushan Bios- phere Reserve”, 1998.
dc.relation/*ref*/J. Zhanga, J. Hud, J. Liane, Z. Fane, X. Ouyange, W. Yee, “Seeing the forest from drones: Testing the potential of light- weight drones as a tool for long-term forest monitoring”, Bio- logical Conservation, Vol. 198, Junio, 2016, pp. 60-69.
dc.relation/*ref*/G. Zhao, H. Hoffmann, J. Yeluripati, S. Xenia, C. Nendel, E. Coucheney, M. Kuhnert, F. Tao, J. Constantin, H. Raynal, E. Teixeira, B. Grosz, L. Doro, R. Kiese, H. Eckersten, E. Haas, D. Cammarano, B. Kassie, M. Moriondo, G. Trombi, M. Bin- di, “Evaluating the precision of eight spatial sampling sche- mes in estimating regional means of simulated yield for two crops”, Environmental Modelling & Software, Vol. 80, Junio,n2016, pp. 100-112.
dc.relation/*ref*/S. Getzin, K. Wiegand, I. Schöning, “Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles”, 2011. J. Dandois, E. Ellis, “Remote sensing of vegetation structure using computer vision”, 2010.
dc.relation/*ref*/R. Zahawi, J. Dandois, K. Holl, D. Nadwodny, J. Reid, E. Ellis, “Using lightweight unmanned aerial vehicles to moni- tor tropical forest recovery”, 2015.
dc.relation/*ref*/S. Puliti, H. Orka, T. Gobakken, E. Naesset, “Inventory of small forest areas using an Unmanned Aerial System”, 2015.
dc.rightsCopyright (c) 2017 Publicaciones e Investigaciónes-ES
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceMagazine specialized in Engineering; Vol. 10 (2016); 23-37en-US
dc.sourcePublicaciones e Investigación; Vol. 10 (2016); 23-37es-ES
dc.source2539-4088
dc.source1900-6608
dc.subjectIngenieríaes-ES
dc.subjectAgricultura de precisión; cultivos; drones; imágenes multiespectrales; ingeniería agronómica; prevención de plagas; tecnología.es-ES
dc.titleDrones Aplicados a la Agricultura de Precisiónes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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