Streamlining the study of the Tierra del Fuego forest through the use of deep learning
Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such in...
Guardado en:
| Autores principales: | , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/91024 |
| Aporte de: |
| id |
I19-R120-10915-91024 |
|---|---|
| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Machine learning Deep learning Computer vision Trap cameras Forests Image recognition Ñire Antarctic nothofagus |
| spellingShingle |
Ciencias Informáticas Machine learning Deep learning Computer vision Trap cameras Forests Image recognition Ñire Antarctic nothofagus Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
| topic_facet |
Ciencias Informáticas Machine learning Deep learning Computer vision Trap cameras Forests Image recognition Ñire Antarctic nothofagus |
| description |
Understanding plant-herbivorous relationships allows to optimize the way to manage and protect natural spaces. In this paper the study of this relationship in the ñire forests (Nothofagus antarctica) of the province of Tierra del Fuego (Argentina) is approached. Using trap cameras to monitor such interaction offers the opportunity to quickly collect large amounts of data.
However, to take advantage of its potential, a large investment in trained personnel to analyze and filter the images of interest is required. The present work seeks to establish a path to significantly reduce this obstacle using the advances of machine and deep learning in the recognition of objects from images. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio |
| author_facet |
Viera, Leonel González, Federico Soler, Rosina Romano, Lucas Feierherd, Guillermo Eugenio |
| author_sort |
Viera, Leonel |
| title |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
| title_short |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
| title_full |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
| title_fullStr |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
| title_full_unstemmed |
Streamlining the study of the Tierra del Fuego forest through the use of deep learning |
| title_sort |
streamlining the study of the tierra del fuego forest through the use of deep learning |
| publishDate |
2019 |
| url |
http://sedici.unlp.edu.ar/handle/10915/91024 |
| work_keys_str_mv |
AT vieraleonel streamliningthestudyofthetierradelfuegoforestthroughtheuseofdeeplearning AT gonzalezfederico streamliningthestudyofthetierradelfuegoforestthroughtheuseofdeeplearning AT solerrosina streamliningthestudyofthetierradelfuegoforestthroughtheuseofdeeplearning AT romanolucas streamliningthestudyofthetierradelfuegoforestthroughtheuseofdeeplearning AT feierherdguillermoeugenio streamliningthestudyofthetierradelfuegoforestthroughtheuseofdeeplearning |
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Repositorios |
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1764820490606608385 |