Hoof Print API is an advanced breed identification tool specifically designed for horses and livestock. Leveraging state-of-the-art image recognition and machine learning techniques, this API enables accurate classification of various breeds by analyzing visual characteristics captured in photographs. It evaluates key features such as coat patterns, body structure, facial morphology, and other distinctive traits unique to each breed. Hoof Print API is an essential resource for farmers, veterinarians, breeders, and livestock management professionals who require precise and automated breed identification.
{"success":true,"image_url":"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTg08a1gWEiGHDWYBt8viqIRGDfyeAYf8pCtQ&s","output":[{"label":"horse","score":0.95}]}
curl --location --request POST 'https://zylalabs.com/api/6386/hoof+print+api/9157/detect+hoof?url=https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTg08a1gWEiGHDWYBt8viqIRGDfyeAYf8pCtQ&s' --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. |
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The Detect Hoof endpoint returns breed identification results based on the submitted image URL. The response includes breed names, confidence scores, and additional characteristics related to the identified breed.
Key fields in the response data include "breed," which specifies the identified breed, "confidence," indicating the accuracy of the identification, and "traits," detailing distinctive features associated with the breed.
The primary parameter for the Detect Hoof endpoint is "image_url," which requires a valid URL pointing to the image of the horse or livestock for analysis.
The response data is organized in a JSON format, containing a main object with fields for breed identification, confidence levels, and any additional traits. This structure allows for easy parsing and integration into applications.
Typical use cases include assisting veterinarians in breed identification, helping breeders select appropriate livestock, and enabling farmers to manage their herds more effectively through accurate breed classification.
Data accuracy is maintained through advanced machine learning algorithms trained on a diverse dataset of images and breeds. Continuous updates and refinements to the model ensure improved identification over time.
Users can expect consistent data patterns, such as a high confidence score for well-defined breeds and lower scores for less distinct or mixed breeds. The response will typically include multiple breed suggestions ranked by confidence.
Users can utilize the returned data by integrating it into livestock management systems, using breed information for breeding decisions, or analyzing traits for health and care requirements specific to identified breeds.
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