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

Research output: Chapter in Book/Report/Conference proceeding › Paper (Conference contribution) › peer-review

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.

Original language	English
Title of host publication	Applied Technologies - 1st International Conference, ICAT 2019, Proceedings
Editors	Miguel Botto-Tobar, Marcelo Zambrano Vizuete, Pablo Torres-Carrión, Sergio Montes León, Guillermo Pizarro Vásquez, Benjamin Durakovic
Publisher	Springer
Pages	473-485
Number of pages	13
ISBN (Print)	9783030425197
DOIs	https://doi.org/10.1007/978-3-030-42520-3_38
State	Published - 1 Jan 2020
Event	1st International Conference on Applied Technologies, ICAT 2019 - Quito, Ecuador Duration: 3 Dec 2019 → 5 Dec 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1194 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Applied Technologies, ICAT 2019
Country	Ecuador
City	Quito
Period	3/12/19 → 5/12/19

Keywords

Convolutional neural network
Deep learning
Precision agriculture
Semantic segmentation
U-Net

Access to Document

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

Link to publication in Scopus

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Quinua Smartapp: Quinoa Smartapp: intelligent rural platform prototype, in real time, to increase productivity in the organic quinoa value chain (chenopodium quinoa willd)
Bedón Monzón, H. M., Garcia Lopez, Y. J. & Carhuancho Lucen, C. A.
6/01/17 → 4/12/19
Project: Research

Cite this

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. In 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).

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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}",

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Paper (Conference contribution) › peer-review

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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 - 2020/1/1

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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.

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KW - Deep learning

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

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A2 - Durakovic, Benjamin

PB - Springer

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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. In Botto-Tobar M, Zambrano Vizuete M, Torres-Carrión P, Montes León S, Pizarro Vásquez G, Durakovic B, editors, Applied Technologies - 1st International Conference, ICAT 2019, Proceedings. Springer. 2020. p. 473-485. (Communications in Computer and Information Science). 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

Abstract

Publication series

Conference

Keywords

Access to Document

Quinua Smartapp: Quinoa Smartapp: intelligent rural platform prototype, in real time, to increase productivity in the organic quinoa value chain (chenopodium quinoa willd)

Cite this