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dc.contributor.author
Sabattini, Julian Alberto
dc.contributor.author
Sturniolo, Francisco
dc.contributor.author
Bollazzi, Martín
dc.contributor.author
Bugnon, Leandro Ariel
dc.date.available
2024-01-15T15:24:09Z
dc.date.issued
2023-10
dc.identifier.citation
Sabattini, Julian Alberto; Sturniolo, Francisco; Bollazzi, Martín; Bugnon, Leandro Ariel; AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior; Elsevier; Smart Agricultural Technology; 5; 100252; 10-2023; 1-7
dc.identifier.issn
2772-3755
dc.identifier.uri
http://hdl.handle.net/11336/223606
dc.description.abstract
Leaf-cutter ants play a crucial role in agroecosystems, and understanding their behavior is key to developing effective damage control strategies. While several tracking solutions exist for ants in controlled environments or on aerial images, accurately measuring ant behavior in the wild remains a challenge. In this work, we propose a three-stage processing pipeline that segments individual ants, tracks their movement, and classifies whether they are carrying a leaf using a convolutional neural network. The output of the pipeline includes a timestamped record of the activity on the trail, accounting for parameters such as ant velocity, size and if it is going from or to the nest. We use the recently developed portable device AntVideoRecord to register video of a selected ant trail. To validate our approach, we collected a labeled dataset and evaluated each stage using standard metrics, achieving a median F2 score of 83% for segmentation, MOTA of 97% for tracking and F1 of 82% for detecting ants carrying a leaf. We then carried out a larger use case in the wild, demonstrating the effectiveness of our approach in capturing the intricate behaviors of leaf-cutter ants. We believe our method has the potential to inform the development of more effective ant damage control strategies in agroecosystems.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
ANT TRACKING
dc.subject
DEEP LEARNING
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IMAGE PROCESSING
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LEAF-CUTTER ANTS
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TRACKING IN THE WILD
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
AntTracker: A low-cost and efficient computer vision approach to research leaf-cutter ants behavior
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-01-11T12:26:31Z
dc.journal.volume
5
dc.journal.number
100252
dc.journal.pagination
1-7
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Sabattini, Julian Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; Argentina
dc.description.fil
Fil: Sturniolo, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Bollazzi, Martín. Universidad de la Republica; Uruguay
dc.description.fil
Fil: Bugnon, Leandro Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; Argentina
dc.journal.title
Smart Agricultural Technology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2772375523000825
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.atech.2023.100252
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