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


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


Palabras clave:

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


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

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. 

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.


Cárdenas López, D., & Arias G., J. C. (2007). Manual de identificación, selección y evaluación de oferta de productos forestales no maderables.

Casapia, X. T., Falen, L., Bartholomeus, H., Cárdenas, R., Flores, G., Herold, M., Coronado, E. N. H., & Baker, T. R. (2019). remote sensing Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery.

Coelho Eugenio, F., Badin, T. L., Fernandes, P., Mallmann, C. L., Schons, C., Schuh, M. S., Soares Pereira, R., Fantinel, R. A., & Pereira da Silva, S. D. (2021). Remotely Piloted Aircraft Systems (RPAS) and machine learning: A review in the context of forest science. In International Journal of Remote Sensing (Vol. 42, Issue 21, pp. 8207–8235). Taylor and Francis Ltd.

Elarab, M., Ticlavilca, A. M., Torres-Rua, A. F., Maslova, I., & McKee, M. (2015). Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 43, 32–42.

Flórez, J., Ortega, J., Betancourt, A., García, A., Bedoya, M., & Botero, J. S. (2020). A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications. TecnoLógicas, 23(48), 269–285.

Fromm, M., Schubert, M., Castilla, G., Linke, J., & McDermid, G. (2019). Automated detection of conifer seedlings in drone imagery using convolutional neural networks. Remote Sensing, 11(21), 2585.

Furley, P. A. (2007). Tropical Forests of the Lowlands. In The Physical Geography of South America. Oxford University Press.

Galeano, A., Urrego, L. E., Sánchez, M., & Peñuela, M. C. (2015). Environmental drivers for regeneration of Mauritia flexuosa L.f. in Colombian Amazonian swamp forest. Aquatic Botany, 123, 47–53.

Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 1440–1448.

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 580–587. / CVPR.2014.81

Gitelson, A. A., Viña, A., Ciganda, V., Rundquist, D. C., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32(8), 1–4.

Gómez-Camperos, J., Jaramillo, H., & Guerrero-Gómez, G. (2022). Técnicas de procesamiento digital de imágenes para detección de plagas y enfermedades en cultivos: una revisión. In Ingeniería Y Competitividad (Issue 00).

He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), 2980–2988. / ICCV.2017.322

Hernández, M.S., Castro, S.Y., Giraldo, B., & Barrera, J. (2018). Seje, moriche, asaí: Palmas amazónicas con potencial (Primera ed). Diana Patricia Mora Rodríguez. FINAL MAIL.pdf

Kahn, F. (1991). Palms as key swamp forest resources in Amazonia. Forest Ecology and Management, 38(3–4), 133–142.

Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164–179.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9905 LNCS, 21–37.

Ma, X., Feng, J., Guan, H., & Liu, G. (2018). Prediction of chlorophyll content in different light areas of apple tree canopies based on the color characteristics of 3d reconstruction. Remote Sensing, 10(3).

Maciel, E. A., & Martins, F. R. (2021). Rarity patterns and the conservation status of tree species in South American savannas. Flora: Morphology, Distribution, Functional Ecology of Plants, 285, 151942.

Mendes, F. N., de Melo Valente, R., Rêgo, M. M. C., & Esposito, M. C. (2017). The floral biology and reproductive system of Mauritia flexuosa (Arecaceae) in a restinga environment in northeastern Brazil. Brittonia, 69(1), 11–25.

Morales, G., Kemper, G., Sevillano, G., Arteaga, D., Ortega, I., & Telles, J. (2018). Automatic segmentation of Mauritia flexuosa in unmanned aerial vehicle (UAV) imagery using deep learning. Forests, 9(12).

Moreira, S. N., Eisenlohr, P. V., Pott, A., Pott, V. J., & Oliveira-Filho, A. T. (2014). Similar vegetation structure in protected and non-protected wetlands in Central Brazil: Conservation significance. Environmental Conservation, 42(4), 356–362.

Navarro-Cruz, A. R., Lazcano-Hernández, M., Vera-López, O., Kammar-García, A., Segura-Badilla, O., Aguilar-Alonso, P., & Pérez-Fernández, M. S. (2021). Mauritia flexuosa L. f. In Fruits of the Brazilian Cerrado (pp. 79–98). Springer, Cham.

Orozco, Ó. A., & Llano Ramírez, G. (2016). Sistemas de Información enfocados en tecnologías de agricultura de precisión y aplicables a la caña de azúcar, una revisión. In Revista Ingenierías Universidad de Medellín (Vol. 15, Issue 28, pp. 103–124).

Páscoa, R. N. M. J., Lopo, M., Teixeira dos Santos, C. A., Graça, A. R., & Lopes, J. A. (2016). Exploratory study on vineyards soil mapping by visible/near-infrared spectroscopy of grapevine leaves. Computers and Electronics in Agriculture, 127, 15–25.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). YOLOv1. Cvpr, 2016-Decem, 779–788.

Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.

SINCHI. (2018). Fichas Palmas amazónicas con potencial Seje, Moriche y Asaí.

Smith, A. M., Bourgeois, G., Teillet, P. M., Freemantle, J., & Nadeau, C. (2014). A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: a case study in wheat. Https://Doi.Org/10.5589/M08-071, 34(6), 539–548.

Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation —A review. Information Processing in Agriculture, 7(1), 1–19.

Urbahs, A., & Jonaite, I. (2013). Features of the use of unmanned aerial vehicles for agriculture applications. Aviation, 17(4), 170–175.

Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Zhu, H., Sun, D., Zhou, W., & He, Y. (2018). Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sensing, 10(9).

Zhang, X., Zhang, F., Qi, Y., Deng, L., Wang, X., & Yang, S. (2019). New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV). International Journal of Applied Earth Observation and Geoinformation, 78(December 2018), 215–226.




Cómo citar

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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