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: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

6 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 estadounidense
Título de la publicación alojada	Applied Technologies - 1st International Conference, ICAT 2019, Proceedings
Editores	Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
Editorial	Springer
Páginas	473-485
-	13
ISBN (versión impresa)	9783030425197
DOI	https://doi.org/10.1007/978-3-030-42520-3_38
Estado	Indizado - 2020
Publicado de forma externa	Sí
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

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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

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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

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. Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. 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).

@inproceedings{a37527a4aeb7429596c344605769464b,

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

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.",

keywords = "Convolutional neural network, Deep learning, Precision agriculture, Semantic segmentation, U-Net",

author = "{Chicch{\'o}n Apaza}, {Miguel {\'A}ngel} and Monz{\'o}n, {H{\'e}ctor Manuel Bed{\'o}n} and Garrido, {Ram{\'o}n Pablo Alcarria}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Applied Technologies, ICAT 2019 ; Conference date: 03-12-2019 Through 05-12-2019",

year = "2020",

doi = "10.1007/978-3-030-42520-3_38",

language = "American English",

isbn = "9783030425197",

series = "Communications in Computer and Information Science",

publisher = "Springer",

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editor = "Miguel Botto-Tobar and {Zambrano Vizuete}, Marcelo and Pablo Torres-Carri{\'o}n and {Montes Le{\'o}n}, Sergio and {Pizarro V{\'a}squez}, Guillermo and Benjamin Durakovic",

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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.), Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. 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

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.
Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. 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: Libro o Capítulo del libro › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net

AU - Chicchón Apaza, Miguel Ángel

AU - Monzón, Héctor Manuel Bedón

AU - Garrido, Ramón Pablo Alcarria

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Convolutional neural network

KW - Deep learning

KW - Precision agriculture

KW - Semantic segmentation

KW - U-Net

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DO - 10.1007/978-3-030-42520-3_38

M3 - Conference contribution

AN - SCOPUS:85082396508

SN - 9783030425197

T3 - Communications in Computer and Information Science

SP - 473

EP - 485

BT - Applied Technologies - 1st International Conference, ICAT 2019, Proceedings

A2 - Botto-Tobar, Miguel

A2 - Zambrano Vizuete, Marcelo

A2 - Torres-Carrión, Pablo

A2 - Montes León, Sergio

A2 - Pizarro Vásquez, Guillermo

A2 - Durakovic, Benjamin

PB - Springer

T2 - 1st International Conference on Applied Technologies, ICAT 2019

Y2 - 3 December 2019 through 5 December 2019

ER -

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, Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. Springer. 2020. p. 473-485. (Communications in Computer and Information Science). 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

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