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

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

11 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 originalInglés
Título de la publicación alojada2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings
EditoresAlvaro David Orjuela-Canon, Diana Briceno Rodriguez
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781538667408
DOI
EstadoPublicada - 5 oct. 2018
Evento1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Medellin, Colombia
Duración: 16 may. 201818 may. 2018

Serie de la publicación

Nombre2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence, ColCACI 2018 - Proceedings

Conferencia

Conferencia1st IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2018
País/TerritorioColombia
CiudadMedellin
Período16/05/1818/05/18

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