Plumage Match API is an intelligent solution designed for the accurate detection of bird breeds and species through advanced image analysis and machine learning. Its primary goal is to provide users with a fast and reliable platform to identify bird species by analyzing key visual characteristics captured in images. The API evaluates features such as plumage coloration, feather patterns, body structure, and distinctive morphological details to differentiate between bird breeds and species with high precision. It is ideal for ornithologists, researchers, birdwatchers, and ecological studies requiring automated and accurate bird classification.
{"success":true,"image_url":"https://debspark.audubon.org/sites/default/files/styles/bean_wysiwyg_full_width/public/western_tanager_usfws.jpg?itok=0htXzQbf","output":[{"label":"Western Tanager","score":0.95}]}
curl --location --request POST 'https://pr197-testing.zylalabs.com/api/6384/plumage+match+api/9155/bird+detection?url=https://debspark.audubon.org/sites/default/files/styles/bean_wysiwyg_full_width/public/western_tanager_usfws.jpg?itok=0htXzQbf' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
|---|---|
Authorization
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[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Bird detection endpoint returns detailed information about identified bird species, including species name, breed, confidence score, and key visual characteristics such as plumage coloration and feather patterns.
The key fields in the response data typically include "species_name," "breed," "confidence_score," and "visual_features," which detail the bird's identifying characteristics.
The primary parameter for the Bird detection endpoint is the "url," which should point to an image of the bird. Ensure the image is clear and well-lit for optimal results.
The response data is organized in a JSON format, with a main object containing the identified species and its attributes, allowing for easy parsing and integration into applications.
The data is sourced from a combination of ornithological databases, field studies, and machine learning models trained on a diverse set of bird images to ensure comprehensive coverage and accuracy.
Typical use cases include birdwatching applications, ecological research, educational tools for ornithology, and automated identification systems for wildlife monitoring.
Users can utilize the returned data by integrating it into applications for real-time bird identification, generating reports for research, or enhancing user experiences in birdwatching platforms.
Data accuracy is maintained through continuous model training with new images, validation against expert-reviewed datasets, and regular updates to the underlying algorithms to improve identification precision.
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