Sistema de visión por computadora para la identificación de palma amazónica y el estado de madurez de sus frutos mediante navegación aérea no tripulada UAV

Autores/as

  • Willintong Marín Rodríguez Pontificia Universidad Javeriana
  • Julián David Colorado Montaño Pontificia Universidad Javeriana
  • Iván Fernando Mondragón Bernal Pontificia Universidad Javeriana

DOI:

https://doi.org/10.26507/paper.2270

Palabras clave:

Palma amazónica, Aprendizaje profundo, Aprendizaje automático, Drones para agricultura, Estado de madurez, Fruto amazónico

Resumen

Se presenta un enfoque de trabajo de investigación haciendo uso de herramientas de visión por computadora y aeronaves no tripuladas UAV para la identificación de palma amazónica (Asai, Seje y Moriche) y el estado de madurez de sus frutos, mediante correlaciones entre el estado de la planta a nivel dosel basados en las radiaciones fotosintéticas y el estado de madurez del fruto.

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Biografía del autor/a

Willintong Marín Rodríguez, Pontificia Universidad Javeriana

Willintong Marin is an Electronic Engineer from the University of Cundinamarca with a master’s
degree in Business Administration, graduated in 2013 from the Externado University of Colombia.
He is currently studying for a Doctorate in Engineering at the Pontificia Universidad Javeriana.
He is interested in the field of research oriented to precision agriculture to explore alternatives
for sustainable use in the Colombian Amazon using Unmanned Aerial Vehicles. With 16 years of
professional experience. He has worked in sectors and activities of administration, education and
engineering.

Julián David Colorado Montaño, Pontificia Universidad Javeriana

Julian D. Colorado is an Associate Professor in the Department of Electronics Engineering at Pontificia
Universidad Javeriana in Bogota, Colombia. He completed his Ph.D and M.Sc. in Robotics at Universidad Politecnica de Madrid in Spain, where he studied the development of novel flight controllers for a diverse category of Unmanned Aerial Vehicles, including quad-rotors and highly articulated morphing wing drones inspired by the biomechanics of bats. He was a visiting research fellow at Brown University, USA (2010–2011), where he studied how to integrate smart-actuators based on Shape Memory Alloys to control wing modulation in a bat-like UAV. Julian Colorado research interests include Field Robotics, Aerial Robotics, Bio-inspired robotics and Guidance Navigation Control –GNC. www.javeriana.edu.co/blogs/coloradoj/ 

Iván Fernando Mondragón Bernal, Pontificia Universidad Javeriana

Ivan Mondragon studied electric engineering at Universidad Nacional de Colombia, obtaining the
degree of Electric Engineer (BSEE) in October 2002. He joined the master program at Universidad
de los Andes (Colombia) obtaining a M.Sc. in Electronics and Computers Engineering in May 2005.
From 2005 to 2006 he worked as Power Transformers Test Field Engineer at Siemens Andina S.A.
(Colombia). After it, he moved to Computer Vision Group at DISAM -ETSII- Universidad Politécnica
de Madrid (Spain) obtaining a Ph.D degree in Automatic and Robotics in November 2011. While
completing his Ph.D he gained extensive experience with Unmanned Aerial Vehicles and in particular
in vision techniques for control and navigation of Autonomous Helicopter and Multirotor platforms.
From August 2013 to February 2019, he collaborates as editor of Journal of Intelligent Robotic Systems
JINT. Since 2013, he is a full-time professor and director of Industrial Automation Technology
Center (CTAI), Department of Industrial Engineering at Pontificia Universidad Javeriana. He is
currently working on computer vision applied to Unmanned Aerial Vehicles as well as Flexible
90 Manufacturing Systems FMS, Quality Inspection, virtual reality (CAVE system) and Industry 4.0.

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Publicado

07-09-2022

Cómo citar

[1]
W. Marín Rodríguez, J. D. Colorado Montaño, y I. F. Mondragón Bernal, «Sistema de visión por computadora para la identificación de palma amazónica y el estado de madurez de sus frutos mediante navegación aérea no tripulada UAV», EIEI ACOFI, sep. 2022.
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