Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net

Miguel Ángel Chicchón Apaza, Héctor Manuel Bedón Monzón, Ramón Pablo Alcarria Garrido

Resultado de la investigación: Capítulo del libro/informe/acta de congresoArticulo (Contribución a conferencia)

Resumen

A first step in the process of automating weed removal in precision agriculture is the semantic segmentation of crops, weeds and soil. Deep learning techniques based on convolutional neural networks are successfully applied today and one of the most popular network architectures in semantic segmentation problems is U-Net. In this article, the variants in the U-Net architecture were evaluated based on the aggregation of residual and recurring blocks to improve their performance. For training and testing, a set of data available on the Internet was used, consisting of 60 multispectral images with unbalanced pixels, so techniques were applied to increase and balance the data. Experimental results show a slight increase in quality metrics compared to the classic U-Net architecture.

Idioma originalInglés
Título de la publicación alojadaApplied Technologies - 1st International Conference, ICAT 2019, Proceedings
EditoresMiguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
EditorialSpringer
Páginas473-485
Número de páginas13
ISBN (versión impresa)9783030425197
DOI
EstadoPublicada - 1 ene 2020
Evento1st International Conference on Applied Technologies, ICAT 2019 - Quito, Ecuador
Duración: 3 dic 20195 dic 2019

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1194 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia1st International Conference on Applied Technologies, ICAT 2019
PaísEcuador
CiudadQuito
Período3/12/195/12/19

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    Chicchón Apaza, M. Á., Monzón, H. M. B., & Garrido, R. P. A. (2020). Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net. En M. Botto-Tobar, M. Zambrano Vizuete, P. Torres-Carrión, S. Montes León, G. Pizarro Vásquez, & B. Durakovic (Eds.), Applied Technologies - 1st International Conference, ICAT 2019, Proceedings (pp. 473-485). (Communications in Computer and Information Science; Vol. 1194 CCIS). Springer. https://doi.org/10.1007/978-3-030-42520-3_38