Object recognition algorithm for the automatic identification and removal of invasive fish

Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing th...

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Bibliographic Details
Published in:Biosystems engineering Vol. 145; pp. 65 - 75
Main Authors: Zhang, Dong, Lee, Dah-Jye, Zhang, Meng, Tippetts, Beau J., Lillywhite, Kirt D.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2016
Subjects:
Algorithms
Automation
biodiversity
Classification
Computer vision
data collection
ECO-Features
Economics
ecosystems
experts
financial economics
Fish
Fish species recognition
humans
indigenous species
invasive species
Invasive species monitoring
monitoring
Monitors
Object recognition
Standard deviation
statistical analysis
Computer vision
Invasive species monitoring
Fish species recognition
ECO-Features
ISSN:1537-5110, 1537-5129
Online Access:Get full text
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Summary:Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing them is the best way to reduce the long-term cost of eradication or control. This paper proposes an object recognition algorithm to automatically identify fish species. Our previous work on general object recognition, called Evolution-COnstructed (ECO) Features, was modified and adapted to construct features and use AdaBoost to classify different fish species. The proposed algorithm does not depend on human experts to design features for fish species classification, but constructs efficient features automatically. Results from experiments show the proposed method obtained an average of 98.9% classification accuracy with a standard deviation of 0.96% with a dataset composed of 8 fish species and a total of 1049 images. Using this algorithm, a fish monitoring system can be built to remove invasive species and monitor native fish abundance, distribution, and size with minimal collateral impact and fish suffering. [Display omitted] •We discuss the importance of invasive fish species removal.•We propose to use Evolution COnstructed (ECO) Features for fish species recognition.•We discuss the uniqueness and advantages of ECO-Features.•Eight fish species including invasive and non-invasive are included for experiments.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1537-5110
1537-5129
DOI:10.1016/j.biosystemseng.2016.02.013