AIMC Topic: Animals, Wild

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Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.

Scientific reports
Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning mode...

A framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications.

PloS one
There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge ...

To save wildlife from fences, scientists turn to AI.

Science (New York, N.Y.)
The research uses aerial imagery to pinpoint structures that could block migratory pronghorn and other wildlife.

Characterizing feral swine movement across the contiguous United States using neural networks and genetic data.

Molecular ecology
Globalization has led to the frequent movement of species out of their native habitat. Some of these species become highly invasive and capable of profoundly altering invaded ecosystems. Feral swine (Sus scrofa × domesticus) are recognized as being a...

Early warning of trends in commercial wildlife trade through novel machine-learning analysis of patent filing.

Nature communications
Unsustainable wildlife trade imperils thousands of species, but efforts to identify and reduce these threats are hampered by rapidly evolving commercial markets. Businesses trading wildlife-derived products innovate to remain competitive, and the pat...

Prevalence of Avian Influenza Virus in Atypical Wild Birds Host Groups during an Outbreak of Highly Pathogenic Strain EA/AM H5N1.

Transboundary and emerging diseases
The global outbreak of highly pathogenic avian influenza (HPAI) H5N1 Eurasian lineage goose/Guangdong clade 2.3.4.4b virus that was detected in North America in 2021 is the largest in history and has significantly impacted wild bird populations and d...

Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism.

Scientific reports
Wild bird repulsion is critical in agriculture because it helps avoid agricultural food losses and mitigates the risk of avian influenza. Wild birds transmit avian influenza in poultry farms and thus cause large economic losses. In this study, we dev...

Predicting chronic wasting disease in white-tailed deer at the county scale using machine learning.

Scientific reports
Continued spread of chronic wasting disease (CWD) through wild cervid herds negatively impacts populations, erodes wildlife conservation, drains resource dollars, and challenges wildlife management agencies. Risk factors for CWD have been investigate...

Phylogenetic and Molecular Characteristics of Wild Bird-Origin Avian Influenza Viruses Circulating in Poland in 2018-2022: Reassortment, Multiple Introductions, and Wild Bird-Poultry Epidemiological Links.

Transboundary and emerging diseases
Since 2020, a significant increase in the severity of H5N highly pathogenic avian influenza (HPAI) epidemics in poultry and wild birds has been observed in Poland. To further investigate the genetic diversity of HPAI H5N viruses of clade 2.3.4.4b, HP...

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

PloS one
Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine...