CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS

Fedra Trujillano; Jessenia Gonzalez; Carlos Saito; Andres Flores; Daniel Racoceanu

doi:10.1109/IGARSS47720.2021.9553826

CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS

Fedra Trujillano
, Jessenia Gonzalez
, Carlos Saito
, Andres Flores
, Daniel Racoceanu

Sección de Electricidad y Electrónica

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

Abstract

Corn is cultivated by smallholder farmers in Ancash - Peru and it is one of the most important crops of the region. Climate change and migration from rural to urban areas are affecting agricultural production and therefore, food security. Information about the cultivated extension is needed for the authorities in order to evaluate the impact in the region. The present study proposes corn areas segmentation in multi-spectral images acquired from Unmanned Aerial Vehicles (UAV), using convolutional neural networks. U-net and U-net using VGG11 encoder were compared using dice and IoU coefficient as metrics. Results show that with the second model, 81.5% dice coefficient can be obtained in this challenging task, allowing envisaging an effective and efficient use of this technology, in this hard context.

Original language	English
Title of host publication	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4712-4715
Number of pages	4
ISBN (Electronic)	9781665403696
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9553826
State	Published - 2021
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2021-July

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger
SDG 13 Climate Action

Keywords

Climate change
Corn identification
Semantic segmentation
UAV

Access to Document

10.1109/IGARSS47720.2021.9553826

Cite this

Trujillano, F., Gonzalez, J., Saito, C., Flores, A., & Racoceanu, D. (2021). CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 4712-4715). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS47720.2021.9553826

Trujillano, Fedra ; Gonzalez, Jessenia ; Saito, Carlos et al. / CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS. IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 4712-4715 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{14d70569aa5046bcacf1d0f6fbf4d3a1,

title = "CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS",

abstract = "Corn is cultivated by smallholder farmers in Ancash - Peru and it is one of the most important crops of the region. Climate change and migration from rural to urban areas are affecting agricultural production and therefore, food security. Information about the cultivated extension is needed for the authorities in order to evaluate the impact in the region. The present study proposes corn areas segmentation in multi-spectral images acquired from Unmanned Aerial Vehicles (UAV), using convolutional neural networks. U-net and U-net using VGG11 encoder were compared using dice and IoU coefficient as metrics. Results show that with the second model, 81.5\% dice coefficient can be obtained in this challenging task, allowing envisaging an effective and efficient use of this technology, in this hard context.",

keywords = "Climate change, Corn identification, Semantic segmentation, UAV",

author = "Fedra Trujillano and Jessenia Gonzalez and Carlos Saito and Andres Flores and Daniel Racoceanu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9553826",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",

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Trujillano, F, Gonzalez, J, Saito, C, Flores, A & Racoceanu, D 2021, CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS. in IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2021-July, Institute of Electrical and Electronics Engineers Inc., pp. 4712-4715, 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium, 12/07/21. https://doi.org/10.1109/IGARSS47720.2021.9553826

CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS. / Trujillano, Fedra; Gonzalez, Jessenia; Saito, Carlos et al.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 4712-4715 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July).

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

TY - GEN

T1 - CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS

AU - Trujillano, Fedra

AU - Gonzalez, Jessenia

AU - Saito, Carlos

AU - Flores, Andres

AU - Racoceanu, Daniel

PY - 2021

Y1 - 2021

N2 - Corn is cultivated by smallholder farmers in Ancash - Peru and it is one of the most important crops of the region. Climate change and migration from rural to urban areas are affecting agricultural production and therefore, food security. Information about the cultivated extension is needed for the authorities in order to evaluate the impact in the region. The present study proposes corn areas segmentation in multi-spectral images acquired from Unmanned Aerial Vehicles (UAV), using convolutional neural networks. U-net and U-net using VGG11 encoder were compared using dice and IoU coefficient as metrics. Results show that with the second model, 81.5% dice coefficient can be obtained in this challenging task, allowing envisaging an effective and efficient use of this technology, in this hard context.

AB - Corn is cultivated by smallholder farmers in Ancash - Peru and it is one of the most important crops of the region. Climate change and migration from rural to urban areas are affecting agricultural production and therefore, food security. Information about the cultivated extension is needed for the authorities in order to evaluate the impact in the region. The present study proposes corn areas segmentation in multi-spectral images acquired from Unmanned Aerial Vehicles (UAV), using convolutional neural networks. U-net and U-net using VGG11 encoder were compared using dice and IoU coefficient as metrics. Results show that with the second model, 81.5% dice coefficient can be obtained in this challenging task, allowing envisaging an effective and efficient use of this technology, in this hard context.

KW - Climate change

KW - Corn identification

KW - Semantic segmentation

KW - UAV

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U2 - 10.1109/IGARSS47720.2021.9553826

DO - 10.1109/IGARSS47720.2021.9553826

M3 - Conference contribution

AN - SCOPUS:85126031080

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 4712

EP - 4715

BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

Trujillano F, Gonzalez J, Saito C, Flores A, Racoceanu D. CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 4712-4715. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS47720.2021.9553826

CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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