Upload a photo of any bird to identify the species, habitat, diet, and migration pattern. The AI bird identifier covers songbirds, raptors, waterfowl, and thousands of species worldwide.
Tap or drag a bird photo here to scan
AI bird identification uses machine learning to recognize bird species from photographs. The user uploads a photo, and the model analyzes plumage, beak shape, body proportions, and other visual features to return a species name. Results include habitat, diet, and migration information when available.
The bird identifier uses a neural network trained on millions of labeled bird photographs. It evaluates plumage color and pattern, beak shape and size, body proportions, tail length, and eye markings. These combined visual signals produce a species match weighted by confidence. The model accounts for variation in pose, lighting conditions, and seasonal plumage differences.
The scanner covers songbirds, raptors, waterfowl, shorebirds, wading birds, woodpeckers, hummingbirds, and gamebirds. Common North American, European, and tropical species are well-represented. AllScan AI also supports animal identification for mammals and other wildlife. Rare and endemic island species have fewer training examples, which may reduce confidence. The model continues to improve as more labeled data becomes available.
Birdwatchers photograph an unfamiliar species at a feeder and want a quick identification. Hikers spot a raptor soaring overhead and wonder what bird is this circling above. A homeowner hears an unfamiliar call at dawn and photographs the source perched on a fence. These are common situations where bird identification by photo provides immediate, practical answers. The same approach works for other outdoor encounters — the insect scanner helps identify bugs and butterflies found in the same habitats.
Use a telephoto lens or phone zoom to get as close as possible without disturbing the bird. A side profile showing the head, body, wing, and tail is ideal. Avoid silhouette shots with the sky directly behind the bird. Natural light from the side or front illuminates plumage colors for the scanner.
Upload the image to the bird identifier. The AI isolates the bird from the background and analyzes visible features. No species guess or text input is required from the user. Processing completes in a few seconds.
The scanner displays the species name, family, and confidence score. Results may include typical habitat, diet, nesting behavior, and migration status. If the confidence is low, the scanner may suggest multiple possible species. Uploading a different angle or better-lit photo often narrows the result.
Feeder birds are among the most frequently scanned. Northern cardinals, blue jays, American goldfinches, black-capped chickadees, and house sparrows are identified with high confidence. The scanner also recognizes less common feeder visitors such as evening grosbeaks, red-bellied woodpeckers, and pine siskins. A photo taken through a window works if the bird is in focus and well-lit.
Hawks, eagles, falcons, owls, and osprey are covered in the model. Perched raptors with visible talons and facial features are identified most accurately. Flight silhouettes are more challenging because plumage detail is harder to resolve at distance. A red-tailed hawk perched on a fence post, for example, returns a high-confidence match based on its distinctive tail coloring and banded belly.
Ducks, geese, swans, herons, egrets, sandpipers, and plovers are included. For aquatic species beyond waterbirds, the fish identification tool covers freshwater and saltwater fish. The AI reads bill shape, body posture, leg length, and plumage markings. Male ducks in breeding plumage are easier to identify than females or juveniles, which share similar brown coloring across species. Shorebird identification often requires visible leg color and bill length for accurate matching.
Many bird species change appearance between breeding and non-breeding seasons. A male American goldfinch is bright yellow in summer and olive-brown in winter. The model accounts for these seasonal variations but may return lower confidence during transitional periods. Juvenile birds often lack adult markings, which makes species-level identification more difficult. The scanner may return a family-level match instead of a species name for juveniles.
Species results may include migration notes such as "neotropical migrant" or "year-round resident." Habitat descriptions cover ecosystem types like deciduous forest, grassland, coastal marsh, or urban areas. Birders who also enjoy identifying trees and flowers in these habitats can use the plant scanner for botanical species. This information is based on the species' typical range and does not reflect real-time sightings. For current bird activity in your area, dedicated birding databases provide more localized data.
AI bird identification is a visual estimate and carries a confidence score that reflects uncertainty. The model does not identify birds by sound through the web scanner. Species with nearly identical plumage, such as Empidonax flycatchers, may not be distinguished without behavioral or vocal cues that a photo cannot capture.
Heavily cropped photos, extreme zoom with digital noise, and backlit silhouettes reduce accuracy. The scanner does not determine the sex of a bird unless sexual dimorphism is visually obvious. Rare vagrants outside their normal range may receive an incorrect match to a more common local species. For scientific documentation or rare bird reporting, confirm AI results with a field guide or local birding authority.
A new birdwatcher sets up a feeder and photographs a small gray bird with a black cap and white cheeks. The scanner identifies it as a black-capped chickadee, a common feeder species across northern North America. The result includes a note about its preference for sunflower seeds, confirming the feeder choice was effective.
During a coastal walk, you photograph a large white bird standing motionless in shallow water. The bird scanner returns "great egret" with notes on its fish-based diet and preference for wetland habitats. That identification adds a recorded species to your birdwatching list.
A homeowner finds a small nest with speckled eggs under their porch eave. They photograph the adult bird returning to the nest and identify it as a house finch. Knowing the species helps them decide to leave the nest undisturbed until the fledglings leave on their own.
A wildlife photographer captures a hawk mid-flight and wants confirmation of the species. The AllScan AI app scans the image and returns "Cooper's hawk" based on the barred chest, rounded tail, and size profile. The photographer uses the species name when submitting the photo to a nature magazine.
AI bird identification works by analyzing a photo against a model trained on thousands of species. The AI examines plumage color, beak shape, body size, wing pattern, and tail structure to return a species match with a confidence score.
Yes. Backyard feeder photos are among the most common uploads. Cardinals, blue jays, chickadees, finches, and sparrows are identified reliably. A side profile showing the full body produces the clearest results.
Upload a photo of the bird to the scanner. Common window visitors include robins, cardinals, wrens, and mourning doves. The AI returns the species name, diet, and regional habitat information.
AllScan AI offers free bird scans through the web tool and mobile app. The web version provides a limited number of daily scans. The app includes additional free scans each day on iOS and Android.
Accuracy is high for common species with distinct plumage. Birds with seasonal changes, juvenile coloring, or female variants of dimorphic species may produce lower confidence results. Clear, well-lit photos improve accuracy.
The web scanner identifies birds from photos only. It does not process audio. After identifying a bird visually, some results include notes about the species' typical call or song description.
Yes. Hawks, eagles, falcons, owls, and vultures are covered. The AI uses wingspan silhouette, beak curvature, and facial disk shape. Raptors photographed in flight from below are harder to identify than perched birds.
Ducks, geese, herons, pelicans, and sandpipers are included. The model distinguishes species by bill shape, body posture, and plumage pattern. Breeding and non-breeding plumage may produce different confidence levels.
A side profile showing the full body is ideal. Include the head, beak, wing, and tail in frame. Avoid silhouettes or backlit photos. Natural light with the bird in focus produces the most reliable identification.
Some results include migration notes, such as whether the bird is a year-round resident, summer breeder, or winter visitor. Migration data is general and based on the species' typical range.
AllScan