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Automated Eggs Recognition Using Ensemble Voting Classifier and Fast Beta Wavelet Network Features
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AI
DOI：10.18280/ts.420532.png)
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Abstract
En 中文
The ability to identify bird species in the nest without having to crack the eggs is a destructive process makes the phenomenon of bird egg recognition of great interest to scientists. Due to this behavior, a significant number of future birds disappear, and the behavior continues. In fact, the visual features of the eggs used by colonial birds as visual signals of identity would allow parents to identify their eggs and avoid the fitness costs associated with providing care to others. The majority of studies have concentrated on an individual's capacity to perform certain behavioral tasks while paying less attention to the egg and how its signals may change over time to improve its identification. Seeing the success of machine learning models in image classification tasks, many studies have been done to classify eggs into the best clutches based on the morphological characteristics of the egg shell. In this work, we built an automatic system to recognize the egg of a slender-billed gull in their best clutches to avoid the genetic test. We present an ensemble voting classifier that uses the Fast Beta Wavelet Network (FBWN) features. Our model has an egg recognition accuracy of 90\%, which has outperformed the state-of-the-art method and shows the efficiency and robustness of our system.
Keywords:
Fast Beta Wavelet Network (FBWN)
egg recognition
ensemble voting classifier
visual feature
Journal
T
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1
Papers:
102
Citations:
1.2K