
Merlin Bird ID by Cornell Lab is a premier bird identification app that helps users identify birds they see and hear. Powered by eBird, the world's largest database of bird sightings, sounds, and photos, Merlin offers four engaging ways to identify birds: answering simple questions, uploading a photo, recording a singing bird, or exploring birds in a region. Whether you're curious about a single bird or aiming to identify every bird you encounter, this free app from the renowned Cornell Lab of Ornithology provides the answers.
With features like expert ID tips, range maps, photos, sounds, and personalized Bird of the Day, Merlin enhances your birding experience. It also uses machine learning through Visipedia for Sound ID and Photo ID, ensuring accurate results based on millions of curated sightings, photos, and sounds. Choose bird packs for anywhere in the world and enjoy content including photos, songs, and calls.
Merlin Bird ID stands out with its unique features designed to enhance bird identification. The app leverages machine learning powered by Visipedia, using millions of photos and sounds collected by birders at eBird.org. This ensures Merlin delivers precise identification results, thanks to experienced birders who curate and annotate data.
Users can enjoy customized lists of birds they can find where they live or travel, anywhere in the world. Merlin also allows users to keep track of their sightings, building a personal list of identified birds. With expert ID tips, range maps, photos, and sounds, Merlin not only identifies birds but also helps users learn about them and improve their birding skills.
Additional features include personalized Bird of the Day and bird packs containing photos, songs, and calls for regions worldwide, including Mexico, Costa Rica, South America, Europe, Africa, the Middle East, India, Australia, Korea, Japan, China, and more.
User-friendly interface
Accurate bird identification
Personalized bird lists
Global bird packs
Free to use
Limited offline functionality
Requires internet connection
Occasional inaccuracies
Heavy data usage
Region-specific content

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