Detection and measurement of butterfly eyespot and spot patterns using convolutional neural networks

Butterflies are increasingly becoming model insects where basic questions surrounding the diversity of their color patterns are being investigated. Some of these color patterns consist of simple spots and eyespots. To accelerate the pace of research surrounding these discrete and circular pattern el...

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Bibliographic Details
Published in:PloS one 2023-02, Vol.18 (2), p.e0280998-e0280998
Main Authors: Cunha, Carolina, Narotamo, Hemaxi, Monteiro, Antónia, Silveira, Margarida
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Animals
Artificial neural networks
Biology and Life Sciences
Butterflies
Butterflies & moths
Color
Color of animals
Comparative analysis
Computer and Information Sciences
Datasets
Deep learning
Digital imaging
Engineering and Technology
Eyespot
Genetic aspects
Health aspects
Identification and classification
Insects
Measurement
Medicine and Health Sciences
Moths
Neural networks
Neural Networks, Computer
Physical Sciences
Physiological aspects
Pigmentation
Research and Analysis Methods
Semantics
Wings
Wings, Animal
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Summary:Butterflies are increasingly becoming model insects where basic questions surrounding the diversity of their color patterns are being investigated. Some of these color patterns consist of simple spots and eyespots. To accelerate the pace of research surrounding these discrete and circular pattern elements we trained distinct convolutional neural networks (CNNs) for detection and measurement of butterfly spots and eyespots on digital images of butterfly wings. We compared the automatically detected and segmented spot/eyespot areas with those manually annotated. These methods were able to identify and distinguish marginal eyespots from spots, as well as distinguish these patterns from less symmetrical patches of color. In addition, the measurements of an eyespot's central area and surrounding rings were comparable with the manual measurements. These CNNs offer improvements of eyespot/spot detection and measurements relative to previous methods because it is not necessary to mathematically define the feature of interest. All that is needed is to point out the images that have those features to train the CNN.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0280998