Understanding Bird Identification Through AI: Strengths and Pitfalls, and Adding Your Own Knowledge and Expertise

By Holly Merker, Birdfy Consultant

Over the past six years, two exciting shifts have transformed birding and bird identification for the better: 1) interest in wild birds and birding has surged, and 2) rapid advances in Artificial Intelligence – AI- have revolutionized our ability identify birds, both by sight and by sound, round-the-clock.

Together, these developments have opened the door to a deeper, more accessible understanding of the birds just outside our windows. Today, a growing suite of apps can identify birds through audio recordings or photographs, powered by increasingly sophisticated AI models. The once–steep learning curve of bird identification hasn’t been erased—but it has been dramatically shortened (and I’ll share more about that nuance later).

Now I can step outside, open an app, and instantly discover who is singing or calling nearby thanks to AI driven apps curated to alert me to nearby bird. I can also see who’s stopping by my bird feeder or bird bath using my Birdfy app, which helps identify each feathered visitor by suggesting an identification based on AI analysis. While this might seem to depart from the essence of birding, these tools aren’t replacing the joy of learning birds—they’re amplifying it, inviting more people into the awareness of the birds around them with ease and excitement.

Harnessing the strength of AI to get to know birds

One of the biggest strengths of AI-powered bird identification tools is their accessibility—they’re user-friendly, easy to learn, and many are free to download with no ongoing subscription. They not only help me explore the birds in my own region but also open a window to species around the world. When paired with devices like Birdfy cameras or yard-based listening stations that record vocalizations or track radio-tagged birds, these tools offer real-time insights on which birds may be around us. If we can’t be outside watching or listening ourselves, the apps pull back the curtain on the birdlife we might otherwise miss. For people like me who can’t monitor my feeders all the time and sometimes travel due to work, my yard-birding “FOMO” has dramatically decreased thanks to my Birdfy app! Additionally, the information provided by these tools has expanded access in meaningful ways, allowing people who may have difficulty hearing or seeing discover which birds are visiting their own backyards. The educational value is immense—these tools help us notice patterns of occurrence, observe behaviors, and witness the daily rituals of our feathered neighbors, deepening our sense of connection to the natural world.

Pitfalls of AI in Bird Identification

Can we rely solely on AI to make identifications for us? You may have noticed that I didn’t say the steep learning curve in birding has been leveled—and that’s because, while AI is an incredible source of knowledge, it’s still learning and evolving as birders too. Just like people, AI improves a little every day, yet even the most advanced models can still get confused. That’s completely understandable when we think about what affects our ability to identify birds.

If I get only a quick glimpse of a fast-moving bird, I might not be able to confidently name it. If I catch it at an odd angle, important features may be hidden. If the bird is in a plumage I don’t often see—like a juvenile or one in the middle of a seasonal molt—I might misidentify it or simply not have enough information to be sure. AI runs into the same challenges. Limited or obstructed views, unusual plumages, background noise, or partial vocalizations can all throw it off. The exciting part is that while AI can be an incredible tutor, we also get to “teach the teacher.” Every corrected identification, every piece of feedback, and every new data point helps these models learn and become more accurate. In many ways, it’s a team effort—and one that allows us to learn with the very tools that are transforming how we experience the bird world.

Understanding the Machine Learning Process

Let’s take a peek at how AI works in the context of bird identification, using a simplified lens to make things easier to understand. At its core, all AI technology is trained by humans. In many ways, AI represents a collaborative bridge—connecting human insight, knowledge, and creativity with the lightning-fast ability of technology to sift through massive datasets and highlight what we’re looking for. It finds, gathers, and shares whatever information we tell it to seek.

Imagine you’re a hungry robin scanning an enormous green field for earthworms. With your own eyes, you’d need to hop, stop, look, and search every patch of ground one worm at a time. But if that robin had AI? The AI would scan the entire field instantly, pinpointing the worms and saving endless time and effort. That’s essentially what AI does for us: it dramatically reduces the time needed to locate information and gives us access to knowledge we might never uncover on our own.

To train AI, we use deep learning—techniques that teach models to recognize patterns. For bird identification, this means exposing AI to vast amounts of data so it can learn to detect patterns in color, bill shape, wing and tail structure, vocalizations, and countless subtle details our eyes might miss. Over time, as these models analyze enormous datasets, they learn to suggest species identifications with impressive accuracy.

And let’s not forget: AI doesn’t learn on its own. These models are shaped through repetition and human input. People like you and me have helped train some of the most widely used bird ID apps available today—I’m proud to have volunteered my time to help teach these systems. Their capabilities didn’t appear overnight; they’ve taken years of training to become the personal birding tutors we now carry in our pockets.

Learning to Identify Birds

So, is there really much difference between how we learn to identify birds and how AI does it?

Reflecting on how I learned bird identification—and how I now teach it to others—I can see that many of the same foundations of learning show up in AI tools as well. Take exposure and repetition, for example: the core fundamentals of how we learn anything. The more often I see something, the more easily I recognize it and can name it. The same is true with people—the more frequently I interact with someone, the more familiar they become. This is how we humans get to know each other, and frankly, how we get to know the birds too.

At its heart, learning is about patterns. Just as AI learns birds by detecting patterns, we learn birds the same way. When we intentionally look and listen for patterns, we accelerate our bird-identification skills. Consider something as simple as encountering a red bird in North America. Not all red birds are Northern Cardinals—but when I see a red bird, I start sorting through the patterns I’ve learned: the black mask around a cardinal’s face (a pattern of color), that distinctive cone-shaped bill (a pattern of shape), the gentle tail-pumping and upright posture (a pattern of behavior), in addition to where I’m seeing the bird (pattern of habit).

These are the same kinds of patterns AI models learn from as they sort through massive datasets. The fundamentals are shared: repeated exposure, pattern recognition, and building familiarity over time. Whether it’s a human birder or an AI model, learning birds begins with noticing patterns.

Side note: when it’s nearly dark outside and a notification from my Birdfy app pops up alerting me that someone has just visited my feeder or birdbath, before even opening the notification, I know who was just there! It’s always the Northern Cardinal. A pattern of behavior that repeats and reveals a lot about this species, thanks to Birdfy!

Training the Trainer

Not all AI tools used in bird identification allow users to give feedback on misidentifications. For those that don’t, it’s important to approach the suggested ID with healthy curiosity and caution. Take it as an invitation to look or listen again—AI is often right, but there’s always the possibility it missed the mark for one of the reasons mentioned earlier.

One of the features I’ve come to truly appreciate in the Birdfy app is its innovative option for user-guided correction. When a questionable ID appears, the app prompts, “Wrong Recognition?” followed by “What’s the species?” This allows the user to choose the correct species tag and provide direct feedback to the system. What I especially love is that when I see a misidentified bird at my Birdfy Feeder 2 Pro or Birdfy Birdbath, clicking on the suggested species name highlights the exact area of the image that led to the AI’s conclusion. A yellow frame appears around the visual cue the model used, giving me a glimpse into its thinking process. Suddenly, I can see precisely what the machine noticed— and why it misinterpreted it.

Even with decades of birding experience, I’m continually learning from seeing how AI interprets identification features—sometimes noticing overlap between species I never would have linked. It feels a bit like a game, one that sharpens my own skills while expanding my understanding.

And that’s the beauty of it—whatever your skill level with bird identification, letting your curiosity guide you opens the door to deeper understanding and more meaningful connections with the birds you meet every day. And AI is just another fantastic tool to help us get in getting to know the birds around us.