SACRA

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BIRD SPECIES IDENTIFICATION

The sounds of the forest are filled with the peeping, chirruping and cheeping of a variety of precious bird species. We use high quality bird recorders and AI-Powered Bird Sound Recognition to record bird calls and document the presence of a diverse range of bird species.

We can obtain a 90% to 99% confidence level in the identification of a species.

Aids in ecological research

Biodiversity monitoring

Conservation efforts

Efficient and non-intrusive data collection

Better understanding of populations and their habitats

Here you can listen to some of the bird species we have documented so far.

Boat-billed Flycatcher

Banded Wren

Golden Vireo

(Megarynchus pitangua)

(Thryophilus pleurostictus)

(Vireo hypochryseus)

Downy Woodpecker

Eastern Wood-Pewee

Warbling Vireo

(Megarynchus pitangua)

(Contopus virens)

(Vireo gilvus

Yellow-winged Cacique

American Crow

Images, common and scientific names taken from iNaturalist database.

(Cassiculus melanicterus)

(Corvus brachyrhynchos)

Scientific Credibility

At SACRA, we base our methodologies on rigorous, peer-reviewed scientific research to ensure the highest standard of accuracy and reliability. Our team integrates data collection with cutting-edge techniques, drawing from established studies to validate our approach in environmental monitoring, land assessment, and carbon capture analysis.

Our methods align with industry standards and contribute to the growing body of knowledge in fields like agroforestry, remote sensing, and carbon sequestration. Below is a selection of the key scientific papers and references that underpin our work:

Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). "BirdNet: A deep learning solution for avian diversity monitoring." Ecological Informatics, 61, 101236.

He, K. S., Bradley, B. A., & Cord, A. F. (2015). "Will remote sensing shape the next generation of species distribution models?" Remote Sensing in Ecology and Conservation, 1(1), 4-18.

Stowell, D., et al. (2019). "Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge." Methods in Ecology and Evolution, 10(3), 368-380.

Priyadarshani, N., Marsland, S., & Castro, I. (2018). "Automated birdsong recognition in complex acoustic environments: A review." Journal of Avian Biology, 49(5), e01761.

This commitment to scientific integrity ensures that the insights we provide to clients are not only accurate but also aligned with the most recent advancements in the field.