Forages ROIs: Automated forage grass detection in aerial imagery

Forages ROIs is a computer vision tool for detecting and classifying forage grasses (Urochloa and Megathyrsus species) in high-resolution UAV imagery. Available as a QGIS plugin and desktop application, it reduces manual annotation time for high-throughput phenotyping applications facilitating crop...

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Bibliographic Details
Main Authors: Ruiz-Hurtado, Andres Felipe, Camelo-Munevar, Rodrigo Andres, Jauregui, Rosa Noemi, Cardoso Arango, Juan Andres
Format: Software
Language:Inglés
Published: 2025
Subjects:
Online Access:https://hdl.handle.net/10568/176858
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author Ruiz-Hurtado, Andres Felipe
Camelo-Munevar, Rodrigo Andres
Jauregui, Rosa Noemi
Cardoso Arango, Juan Andres
author_browse Camelo-Munevar, Rodrigo Andres
Cardoso Arango, Juan Andres
Jauregui, Rosa Noemi
Ruiz-Hurtado, Andres Felipe
author_facet Ruiz-Hurtado, Andres Felipe
Camelo-Munevar, Rodrigo Andres
Jauregui, Rosa Noemi
Cardoso Arango, Juan Andres
author_sort Ruiz-Hurtado, Andres Felipe
collection Repository of Agricultural Research Outputs (CGSpace)
description Forages ROIs is a computer vision tool for detecting and classifying forage grasses (Urochloa and Megathyrsus species) in high-resolution UAV imagery. Available as a QGIS plugin and desktop application, it reduces manual annotation time for high-throughput phenotyping applications facilitating crop grid generation.
format Software
id CGSpace176858
institution CGIAR Consortium
language Inglés
publishDate 2025
publishDateRange 2025
publishDateSort 2025
record_format dspace
spelling CGSpace1768582025-10-07T07:31:40Z Forages ROIs: Automated forage grass detection in aerial imagery Ruiz-Hurtado, Andres Felipe Camelo-Munevar, Rodrigo Andres Jauregui, Rosa Noemi Cardoso Arango, Juan Andres machine learning unmanned aerial vehicles imagery feed crops Forages ROIs is a computer vision tool for detecting and classifying forage grasses (Urochloa and Megathyrsus species) in high-resolution UAV imagery. Available as a QGIS plugin and desktop application, it reduces manual annotation time for high-throughput phenotyping applications facilitating crop grid generation. 2025-05 2025-10-07T07:31:39Z 2025-10-07T07:31:39Z Software https://hdl.handle.net/10568/176858 en Open Access Ruiz-Hurtado, A.F.; Camelo-Munevar, R.A.; Jauregui, R.N.; Cardoso Arango, J.A. (2025) Forages ROIs: Automated forage grass detection in aerial imagery. [Software] URL: https://github.com/afruizh/forages_rois
spellingShingle machine learning
unmanned aerial vehicles
imagery
feed crops
Ruiz-Hurtado, Andres Felipe
Camelo-Munevar, Rodrigo Andres
Jauregui, Rosa Noemi
Cardoso Arango, Juan Andres
Forages ROIs: Automated forage grass detection in aerial imagery
title Forages ROIs: Automated forage grass detection in aerial imagery
title_full Forages ROIs: Automated forage grass detection in aerial imagery
title_fullStr Forages ROIs: Automated forage grass detection in aerial imagery
title_full_unstemmed Forages ROIs: Automated forage grass detection in aerial imagery
title_short Forages ROIs: Automated forage grass detection in aerial imagery
title_sort forages rois automated forage grass detection in aerial imagery
topic machine learning
unmanned aerial vehicles
imagery
feed crops
url https://hdl.handle.net/10568/176858
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