TY - JOUR
T1 - A reference process for automating bee species identification based on wing images and digital image processing
AU - SANTANA, Fabiana
AU - Costa, Anna
AU - Truzzi, Flavio
AU - Silva, Felipe
AU - Santos, Sheila
AU - Francoy, Tiago
AU - Saraiva, Antonio
N1 - Funding Information:
The authors are grateful to the Universidade de São Paulo for the support to the Biodiversity and Computing Research Center, BioComp; to CAPES for the support to Flavio Sales Truzzi and to Sheila Leal Santos; to FAPESP ( proc. n. 2011/07857-9 and 2011/19280-8 ) for the support to Tiago Mauricio Francoy and to Anna Helena Reali Costa; and to CNPq ( proc. n. 311058/2011-6 , 308326/2010-5 and 131238/2013-2 ) for the support to Anna Helena Reali Costa, Antonio Mauro Saraiva, and Felipe Leno da Silva.
Publisher Copyright:
© 2013 Elsevier B.V.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - Pollinators play a key role in biodiversity conservation, since they provide vital services to both natural ecosystems and agriculture. In particular, bees are excellent pollinators; therefore, their mapping, classification, and preservation help to promote biodiversity conservation. However, these tasks are difficult and time consuming since there is a lack of classification keys, sampling efforts and trained taxonomists. The development of tools for automating and assisting the identification of bee species represents an important contribution to biodiversity conservation. Several studies have shown that features extracted from patterns of bee wings are good discriminatory elements to differentiate among species, and some have devoted efforts to automate this process. However, the automated identification of bee species is a particularly hard problem, because (i) individuals of a given species may vary hugely in morphology, and (ii) closely related species may be extremely similar to one another. This paper proposes a reference process for bee classification based on wing images to provide a complete understanding of the problem from the experts' point of view, and a foundation to software systems development and integration using Internet services. The results can be extended to other species identification and taxonomic classification, as long as similar criteria are applicable. The reference process may also be helpful for beginners in this research field, as they can use the process and the experiments presented here as a guide to this complex activity.
AB - Pollinators play a key role in biodiversity conservation, since they provide vital services to both natural ecosystems and agriculture. In particular, bees are excellent pollinators; therefore, their mapping, classification, and preservation help to promote biodiversity conservation. However, these tasks are difficult and time consuming since there is a lack of classification keys, sampling efforts and trained taxonomists. The development of tools for automating and assisting the identification of bee species represents an important contribution to biodiversity conservation. Several studies have shown that features extracted from patterns of bee wings are good discriminatory elements to differentiate among species, and some have devoted efforts to automate this process. However, the automated identification of bee species is a particularly hard problem, because (i) individuals of a given species may vary hugely in morphology, and (ii) closely related species may be extremely similar to one another. This paper proposes a reference process for bee classification based on wing images to provide a complete understanding of the problem from the experts' point of view, and a foundation to software systems development and integration using Internet services. The results can be extended to other species identification and taxonomic classification, as long as similar criteria are applicable. The reference process may also be helpful for beginners in this research field, as they can use the process and the experiments presented here as a guide to this complex activity.
KW - Bee wing classification
KW - Digital image processing
KW - Machine learning
KW - Reference process
KW - Taxonomic classification
UR - http://www.scopus.com/inward/record.url?scp=85027948788&partnerID=8YFLogxK
U2 - 10.1016/j.ecoinf.2013.12.001
DO - 10.1016/j.ecoinf.2013.12.001
M3 - Article
SN - 1574-9541
VL - 24
SP - 248
EP - 260
JO - Ecological Informatics
JF - Ecological Informatics
ER -