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.

Citas

Cárdenas López, D., & Arias G., J. C. (2007). Manual de identificación, selección y evaluación de oferta de productos forestales no maderables. https://sinchi.org.co/manual-de-identificacion-seleccion-y-evaluacion-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. https://doi.org/10.3390/rs12010009

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. https://doi.org/10.1080/01431161.2021.1975845

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. https://doi.org/10.1016/J.JAG.2015.03.017

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. https://doi.org/10.22430/22565337.1408

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. https://doi.org/10.3390/rs11212585

Furley, P. A. (2007). Tropical Forests of the Lowlands. In The Physical Geography of South America. Oxford University Press. https://doi.org/10.1093/oso/9780195313413.003.0017

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. https://doi.org/10.1016/j.aquabot.2015.02.001

Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 1440–1448. https://doi.org/10.1109/ICCV.2015.169

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. https://doi.org/10.1109 / 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. https://doi.org/10.1029/2005GL022688

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. https://doi.org/10.1109 / 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. https://www.sinchi.org.co/files/publicaciones/publicaciones/pdf/MANUAL FINAL MAIL.pdf

Kahn, F. (1991). Palms as key swamp forest resources in Amazonia. Forest Ecology and Management, 38(3–4), 133–142. https://doi.org/10.1016/0378-1127(91)90139-M

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. https://doi.org/10.1016/J.RSE.2016.11.023

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. https://doi.org/10.1007/978-3-319-46448-0_2

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). https://doi.org/10.3390/rs10030429

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. https://doi.org/10.1016/j.flora.2021.151942

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. https://doi.org/10.1007/s12228-016-9444-2

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). https://doi.org/10.3390/f9120736

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. https://doi.org/10.1017/S0376892915000107

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. https://doi.org/10.1007/978-3-030-62949-6_5

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). https://doi.org/10.22395/rium.v15n28a6

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. https://doi.org/10.1016/j.compag.2016.05.014

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. https://doi.org/10.1109/TPAMI.2016.2577031

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. https://doi.org/10.5589/M08-071

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. https://doi.org/10.1016/J.INPA.2019.09.006

Urbahs, A., & Jonaite, I. (2013). Features of the use of unmanned aerial vehicles for agriculture applications. Aviation, 17(4), 170–175. https://doi.org/10.3846/16487788.2013.861224

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). https://doi.org/10.3390/rs10091484

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. https://doi.org/10.1016/j.jag.2019.01.001

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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.

Evento

2022: Encuentro Internacional de Educación en Ingeniería ACOFI 2022

Sección

Estudiantes de doctorado

Licencia

Derechos de autor 2022 Asociación Colombiana de Facultades de Ingeniería - ACOFI

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

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