TY - JOUR
T1 - Automatic ladybird beetle detection using deep-learning models
AU - Venegas, Pablo
AU - Calderon, Francisco
AU - Riofrío, Daniel
AU - Benítez, Diego
AU - Ramón, Giovani
AU - Cisneros-Heredia, Diego
AU - Coimbra, Miguel
AU - Rojo-Álvarez, José Luis
AU - Pérez, Noel
N1 - Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2021/6/10
Y1 - 2021/6/10
N2 - Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.
AB - Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.
UR - http://www.scopus.com/inward/record.url?scp=85107806280&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0253027
DO - 10.1371/journal.pone.0253027
M3 - Article
C2 - 34111201
AN - SCOPUS:85107806280
SN - 1932-6203
VL - 16
SP - 1
EP - 21
JO - PLoS ONE
JF - PLoS ONE
IS - 6
M1 - e0253027
ER -