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

doi:10.1007/978-3-030-42520-3_38

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

Producción científica: Capítulo del libro/informe/acta de congreso › Capítulo › revisión exhaustiva

11 Citas (Scopus)

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 original	Inglés
Título de la publicación alojada	Communications in Computer and Information Science
Editores	Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
Editorial	Springer
Páginas	473-485
Número de páginas	13
ISBN (versión impresa)	9783030425197
DOI	https://doi.org/10.1007/978-3-030-42520-3_38 https://doi.org/10.1007/978-3-030-42520-3_38
Estado	Publicada - 1 ene. 2020
Evento	1st International Conference on Applied Technologies, ICAT 2019 - Quito, Ecuador Duración: 3 dic. 2019 → 5 dic. 2019

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1194 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	1st International Conference on Applied Technologies, ICAT 2019
País/Territorio	Ecuador
Ciudad	Quito
Período	3/12/19 → 5/12/19

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Otros archivos y enlaces

Quinua Smartapp: Quinua Smartapp: prototipo de plataforma rural inteligente, en tiempo real, para incrementar la productividad en la cadena de valor de quinua orgánica (chenopodium quinoa willd)
Bedón Monzón, H. M. (Investigador principal), Garcia Lopez, Y. J. (Investigador adjunto), Carhuancho Lucen, C. A. (Investigador adjunto) & Jimenez Davalos, J. E. (Investigador adjunto)
Instituto Nacional de Innovación Agraria, Fundación para el Desarrollo Agrario, Comunidad campesina distrito San Lorenzo - CCSL
6/01/17 → 4/12/19
Proyecto: Investigación

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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.), Communications in Computer and Information Science (pp. 473-485). (Communications in Computer and Information Science; Vol. 1194 CCIS). Springer. https://doi.org/10.1007/978-3-030-42520-3_38, https://doi.org/10.1007/978-3-030-42520-3_38

Chicchón Apaza, Miguel Ángel ; Monzón, Héctor Manuel Bedón ; Garrido, Ramón Pablo Alcarria. / Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net. Communications in Computer and Information Science. editor / Miguel Botto-Tobar ; Marcelo Zambrano Vizuete ; Pablo Torres-Carrión ; Sergio Montes León ; Guillermo Pizarro Vásquez ; Benjamin Durakovic. Springer, 2020. pp. 473-485 (Communications in Computer and Information Science).

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abstract = "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.",

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Chicchón Apaza, MÁ, Monzón, HMB & Garrido, RPA 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.), Communications in Computer and Information Science. Communications in Computer and Information Science, vol. 1194 CCIS, Springer, pp. 473-485, 1st International Conference on Applied Technologies, ICAT 2019, Quito, Ecuador, 3/12/19. https://doi.org/10.1007/978-3-030-42520-3_38, https://doi.org/10.1007/978-3-030-42520-3_38

Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net. / Chicchón Apaza, Miguel Ángel; Monzón, Héctor Manuel Bedón; Garrido, Ramón Pablo Alcarria.
Communications in Computer and Information Science. ed. / Miguel Botto-Tobar; Marcelo Zambrano Vizuete; Pablo Torres-Carrión; Sergio Montes León; Guillermo Pizarro Vásquez; Benjamin Durakovic. Springer, 2020. p. 473-485 (Communications in Computer and Information Science; Vol. 1194 CCIS).

Producción científica: Capítulo del libro/informe/acta de congreso › Capítulo › revisión exhaustiva

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AU - Garrido, Ramón Pablo Alcarria

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A2 - Durakovic, Benjamin

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Chicchón Apaza MÁ, Monzón HMB, Garrido RPA. Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net. En Botto-Tobar M, Zambrano Vizuete M, Torres-Carrión P, Montes León S, Pizarro Vásquez G, Durakovic B, editores, Communications in Computer and Information Science. Springer. 2020. p. 473-485. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-42520-3_38, 10.1007/978-3-030-42520-3_38

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

Resumen

Serie de la publicación

Conferencia

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Proyectos

Quinua Smartapp: Quinua Smartapp: prototipo de plataforma rural inteligente, en tiempo real, para incrementar la productividad en la cadena de valor de quinua orgánica (chenopodium quinoa willd)

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