Corn classification using Deep Learning with UAV imagery. An operational proof of concept

Fedra Trujillano; Andres Flores; Carlos Saito; Mario Balcazar; Daniel Racoceanu

doi:10.1109/ColCACI.2018.8484845

Corn classification using Deep Learning with UAV imagery. An operational proof of concept

Fedra Trujillano, Andres Flores, Carlos Saito, Mario Balcazar, Daniel Racoceanu

Pontifical Catholic Univ. of Peru

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

13 Citas (Scopus)

Resumen

Climate change is affecting the agricultural production in Ancash - Peru and corn is one of the most important crops of the region. It is essential to constantly monitor grain yields and generate statistic models in order to evaluate how climate change will affect food security. The present study proposes as a proof of concept to use Deep learning techniques for the classification of near infrared images, acquired by an Unmanned Aerial Vehicle (UAV), in order to estimate areas of corn, for food security purpose. The results show that using a well balanced (altitudes, seasons, regions) database during the acquisition process improves the performance of a trained system, therefore facing crop classification from a variable and difficult-to-access geography.

Idioma original	Inglés
Título de la publicación alojada	2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings
Editores	Alvaro David Orjuela-Canon, Diana Briceno Rodriguez
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781538667408
DOI	https://doi.org/10.1109/ColCACI.2018.8484845
Estado	Publicada - 5 oct. 2018
Evento	1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Medellin, Colombia Duración: 16 may. 2018 → 18 may. 2018

Serie de la publicación

Nombre	2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings

Conferencia

Conferencia	1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018
País/Territorio	Colombia
Ciudad	Medellin
Período	16/05/18 → 18/05/18

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/ColCACI.2018.8484845

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Trujillano, F., Flores, A., Saito, C., Balcazar, M., & Racoceanu, D. (2018). Corn classification using Deep Learning with UAV imagery. An operational proof of concept. En A. D. Orjuela-Canon, & D. B. Rodriguez (Eds.), 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings Artículo 8484845 (2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ColCACI.2018.8484845

Trujillano, Fedra ; Flores, Andres ; Saito, Carlos et al. / Corn classification using Deep Learning with UAV imagery. An operational proof of concept. 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings. editor / Alvaro David Orjuela-Canon ; Diana Briceno Rodriguez. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings).

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title = "Corn classification using Deep Learning with UAV imagery. An operational proof of concept",

abstract = "Climate change is affecting the agricultural production in Ancash - Peru and corn is one of the most important crops of the region. It is essential to constantly monitor grain yields and generate statistic models in order to evaluate how climate change will affect food security. The present study proposes as a proof of concept to use Deep learning techniques for the classification of near infrared images, acquired by an Unmanned Aerial Vehicle (UAV), in order to estimate areas of corn, for food security purpose. The results show that using a well balanced (altitudes, seasons, regions) database during the acquisition process improves the performance of a trained system, therefore facing crop classification from a variable and difficult-to-access geography.",

author = "Fedra Trujillano and Andres Flores and Carlos Saito and Mario Balcazar and Daniel Racoceanu",

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Trujillano, F, Flores, A, Saito, C, Balcazar, M & Racoceanu, D 2018, Corn classification using Deep Learning with UAV imagery. An operational proof of concept. En AD Orjuela-Canon & DB Rodriguez (eds.), 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings., 8484845, 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018, Medellin, Colombia, 16/05/18. https://doi.org/10.1109/ColCACI.2018.8484845

Corn classification using Deep Learning with UAV imagery. An operational proof of concept. / Trujillano, Fedra; Flores, Andres; Saito, Carlos et al.
2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings. ed. / Alvaro David Orjuela-Canon; Diana Briceno Rodriguez. Institute of Electrical and Electronics Engineers Inc., 2018. 8484845 (2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Corn classification using Deep Learning with UAV imagery. An operational proof of concept

AU - Trujillano, Fedra

AU - Flores, Andres

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AU - Balcazar, Mario

AU - Racoceanu, Daniel

PY - 2018/10/5

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N2 - Climate change is affecting the agricultural production in Ancash - Peru and corn is one of the most important crops of the region. It is essential to constantly monitor grain yields and generate statistic models in order to evaluate how climate change will affect food security. The present study proposes as a proof of concept to use Deep learning techniques for the classification of near infrared images, acquired by an Unmanned Aerial Vehicle (UAV), in order to estimate areas of corn, for food security purpose. The results show that using a well balanced (altitudes, seasons, regions) database during the acquisition process improves the performance of a trained system, therefore facing crop classification from a variable and difficult-to-access geography.

AB - Climate change is affecting the agricultural production in Ancash - Peru and corn is one of the most important crops of the region. It is essential to constantly monitor grain yields and generate statistic models in order to evaluate how climate change will affect food security. The present study proposes as a proof of concept to use Deep learning techniques for the classification of near infrared images, acquired by an Unmanned Aerial Vehicle (UAV), in order to estimate areas of corn, for food security purpose. The results show that using a well balanced (altitudes, seasons, regions) database during the acquisition process improves the performance of a trained system, therefore facing crop classification from a variable and difficult-to-access geography.

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Trujillano F, Flores A, Saito C, Balcazar M, Racoceanu D. Corn classification using Deep Learning with UAV imagery. An operational proof of concept. En Orjuela-Canon AD, Rodriguez DB, editores, 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8484845. (2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings). doi: 10.1109/ColCACI.2018.8484845

Corn classification using Deep Learning with UAV imagery. An operational proof of concept

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