What is the AI that detects bird types?

In recent years, there has been growing interest in developing artificial intelligence (AI) systems that can automatically identify different species of birds. Identifying birds accurately is a challenging task, even for experienced human birdwatchers. However, training AI algorithms on large datasets of bird images shows promise for creating automated bird identification tools that can match or even exceed human-level accuracy.

Why build an AI to identify birds?

There are several motivations for developing AI bird detectors:

• To assist amateur birdwatchers – Apps powered by AI bird detectors can act as an automatic field guide, helping weekend birders identify the species they spot.
• To enable large-scale ecological surveys – AI systems can process huge volumes of images from camera traps faster and more accurately than humans, scaling up data collection.
• To study birds non-invasively – Automatically identifying bird species in images allows their behavior and populations to be monitored without any need for capturing or banding birds.
• To quantify biodiversity – AI detection provides standardized species identification, allowing better estimates of species distributions, richness and abundance.

In essence, AI bird detectors open up new possibilities for ornithology, conservation, ecology and nature enthusiasts that have not been possible through manual methods alone. The rapid improvement in AI image classification accuracy makes this an especially promising time for developing these technologies.

What types of birds can current AI detectors identify?

Many of the most advanced AI bird detectors have been developed and trained in North America and Europe, putting a focus on species native to those regions. Here are some of the types of birds that current AI tools are capable of identifying:

• Songbirds – Familiar backyard species like chickadees, sparrows, finches and warblers are well represented in training data and can be detected accurately by AI.
• Waterfowl – Large and distinctive waterbirds like ducks, geese, swans, loons and grebes can be readily identified to species.
• Raptors – Birds of prey like hawks, eagles, falcons, ospreys and owls are a common focus for AI bird detectors.
• Wading birds – Herons, egrets, ibises and other long-legged wading birds pose unique challenges but some AI tools are up to the task.
• Shorebirds – An AI system trained on a shorebird dataset was able to identify over 30 Californian shorebird species with high accuracy.

However, AI detectors are weakest for less studied taxonomic groups like seabirds and nocturnal species where training data is scarce. There are also many parts of the world lacking comprehensive training data, limiting AI identification abilities in those regions. But new datasets are helping expand the diversity of birds covered.

How do the AI bird detectors work?

Most current AI systems for bird identification rely on a cutting-edge machine learning technique called deep convolutional neural networks. Here’s a quick overview of how these bird detectors operate:

1. A large training dataset containing thousands of labeled bird photos is compiled, covering the species to be identified.
2. The neural network analyzes the training images and develops hierarchical features for recognizing key bird parts.
3. The network tunes millions of internal parameters to create a detection model that can classify incoming bird images.
4. New images of birds are fed into the finished model, which predicts the species based on visual pattern matching to the training data.
5. The neural network keeps improving its accuracy as it processes more bird images and fine-tunes its connections through repetitive training.

So in essence, the AI model builds up an abstract representation of the visual features diagnostic for each species by exposing it to many examples. This allows it to match new bird photographs to its accumulated knowledge. The model’s architecture gives it the ability to handle variances in bird position, background, lighting conditions, etc. while still recognizing the essential features that define a particular species for accurate classification.

What accuracy can these AI systems achieve?

State-of-the-art deep learning bird classifiers are now competitive with top human experts when identifying birds from images. For example, in the 2020 NABirds competition, the best AI model attained over 95% accuracy classifying 555 North American bird species, surpassing 92% accuracy by leading bird experts.

For large but less diverse bird datasets, AI accuracy can be near perfect. One model achieved over 98% accuracy identifying 200 common British bird species. The Patuxent Wildlife Research Center achieved 97.2% accuracy classifying 100 backyard Maryland bird species including confusing lookalikes.

However, performance drops significantly for rare species with few training examples. Accuracy also depends greatly on image quality – poor lighting, obstructed views and juvenile/female birds provide tougher challenges. While promising, there is still room for improvement in expanding the diversity of identifiable species and dealing with trickier bird photos.

What tools and apps use AI bird identification?

Ornithologists have access to powerful supercomputer-based neural networks capable of expert bird classification. But for regular birdwatchers, there are also many user-friendly mobile apps that employ AI to identify birds from photos:

• Merlin Bird ID – One of the most popular apps powered by AI from the Cornell Lab of Ornithology.
• PictureThis – A plant identification app with an additional bird classifier.
• BirdNet – Records bird sounds and uses AI to identify species from songs and calls.
• iBird Pro – Field guide app with AI helper to refine ID suggestions by users.
• Birdzilla – Citizen science app where public submits photos to train and improve its bird classifier.

These tools allow casual birders to easily identify birds in their backyard and beyond. Some also assist professional ornithological projects by processing crowdsourced image submissions from the public. The accessibility of these apps is helping both amateur naturalists and scientific research.

What bird species pose the greatest challenges?

While AI bird detectors excel for many common species, some groups of birds provide special challenges:

• Rare and elusive birds – Insufficient training data exists for endangered, scarce or regionally restricted species.
• Seabirds – Classifiers struggle to identify birds photographed over water from a distance.
• Juvenile and female birds – Often look different than adult males in training photos.
• Lookalike species – Close visual similarities make certain groups of birds harder to distinguish.
• Captive birds – Abnormal features and behaviors can confuse classifiers.

Specialized techniques like few-shot learning, context-aware prediction, and active learning may help overcome these cases by allowing AI models to deal with limited data and adapt to new observations. But expanding training databases to capture more diversity and collaborating with experts to prioritize tricky species will be key to improving accuracy.

What impact could AI bird detectors have on ornithology?

AI promises to revolutionize the field of ornithology much like it has transformed other disciplines:

• Big data – Processing huge volumes of bird photos, sound recordings and other observations that overwhelm human capacity.
• Real-time monitoring – Enabling tracking of migratory patterns, range shifts and population changes as they unfold.
• Automated field studies – Camera traps, drones, robotic birds and other technologies integrated with AI analysis.
• Enhanced human knowledge – Revealing hard-to-observe bird behaviors,interactions and other phenomena.
• Democratized data collection – Public engagement via birder apps enhances and expands datasets.

But there are also concerns over issues like biases in training data, loss of observer skills, reliance on technology companies, and increased surveillance of nature. Maintaining human knowledge, perspective and oversight will remain crucial as AI bird detectors open up new ornithology frontiers.

Conclusion

AI systems capable of identifying bird species from photographs with high accuracy are now within reach, powered by advances in deep neural networks. While challenges remain in expanding coverage to rarer species and varied conditions, the potential of automated bird detection is vast, from accelerating scientific surveys to assisting casual birdwatchers. Moving forward thoughtfully, AI bird detectors promise to be a transformative technology supporting the conservation and appreciation of avian biodiversity worldwide.