How computer vision in zoos can improve animal care

The role of computer vision in zoos and veterinary care

Consider a zookeeper watching over a sick animal, carefully observing its behavior throughout the day to catch any changes. Now, imagine a camera system doing that work continuously - monitoring the animal around the clock and notifying staff as soon as something seems off. Computer vision makes this possible.

Cameras integrated with computer vision can help monitor animals in real time, making it easier to detect early signs of stress, injury, or illness. Models like Ultralytics YOLO11, which support various computer vision tasks, can detect animals, track their movements, and assist with routine tasks such as behavior monitoring.

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Similarly, in veterinary care, computer vision can assist vets with complex tasks, such as diagnosing eye conditions across species with widely varying anatomy. For instance, such tools can accurately detect issues like cataracts or glaucoma, improving both the speed and consistency of diagnosis.

As the need for effective animal care grows, zoos and clinics are adopting computer vision solutions to manage daily operations more efficiently. From early illness detection to automating routine checks, models like YOLO11 help caretakers deliver more consistent, responsive care, especially when resources are limited.

Applications of computer vision in zoos

Now that we’ve seen how computer vision supports animal care in zoos and clinics, let’s take a closer look at some global case studies that show how it’s being used in real-world wildlife conservation and animal health monitoring efforts.

Animal facial recognition: AI monitoring of endangered pandas

The giant panda, once endangered and now considered vulnerable, remains a major focus of conservation efforts. But keeping track of individual pandas, their health, behavior, and reproduction, can be difficult since they have similar facial features. Even trained experts can struggle to distinguish one panda from another. 

Traditional methods like checking footprints or doing DNA tests have been used, but these methods are generally expensive, invasive, and not always reliable.

To tackle this issue, researchers at the Chengdu Research Base of Giant Panda Breeding in China have created a facial recognition system using computer vision. The system uses Vision AI to identify individual pandas by analyzing the subtle, unique features of their faces.

This facial recognition technology lets researchers continuously track pandas, watch their movements, feeding habits, and health without causing any harm or disturbance. It provides an easy way to collect accurate, real-time information about the pandas, making the monitoring process much more effective and dependable.

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AI in animal health monitoring keeps red pandas stress-free

Red pandas are shy and solitary animals that are facing serious threats to their survival. Over the past 18 years, about three generations, their global population has dropped by more than 50%, mainly because of habitat loss and illegal capture. They live in parts of Myanmar, Nepal, India, and China, especially in the Himalayas and western Sichuan, where around 6,000 to 7,000 red pandas remain.

Even in protected environments like zoos, they can still be at risk. Health issues in red pandas often show up as small changes in behavior, like moving less, interacting less, or not sleeping well. However, these signs can be so subtle that they’re easy to miss.

To help protect these endangered animals, researchers are developing new ways to care for them. For instance, data collected from motion-activated cameras at Bifengxia Wildlife World in China was used to train a YOLOv8-based computer vision model designed to monitor red panda behavior.

This model can help caretakers spot early warning signs of stress or illness, such as unusual stillness or reduced activity, without disturbing the animals. It’s a non-invasive approach that allows for round-the-clock monitoring while keeping red pandas comfortable and stress-free.

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Animal tracking with computer vision: Monitoring dolphins

Watching how animals behave in the water, like bottlenose dolphins, can be tricky. They swim fast, dive deep, and often move in groups, which makes it hard for caretakers to track their routines or notice small changes. But understanding how they rest, interact, and use their space is key to keeping them healthy. This is especially important in zoos and aquariums, where their environment needs to be carefully managed.

At Brookfield Zoo in the US, researchers have designed a system that uses computer vision to track the movement of dolphins throughout their enclosure. They have installed high-resolution cameras around the enclosure to record the dolphins moving throughout the day. These recordings are then analyzed to understand behavior patterns like swimming, resting, interacting, or exploring.

Specifically, object tracking is used to follow each dolphin's movement over time. This computer vision technique enables the system to identify individual dolphins and monitor their activities, even when they swim close together or dive underwater. By tracking these patterns, caretakers can gain insights into how dolphins use their space, interact with each other, and respond to environmental changes.

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Pros and cons of computer vision in zoos

Nowadays, computer vision is being used in a variety of animal care settings, from zoos to veterinary clinics, to improve monitoring, support diagnosis, and enhance overall welfare. Here are some key ways it’s helping to improve animal care practices:

• Enclosure use analysis: Heatmaps and behavior tracking show which areas of an enclosure animals prefer or avoid, helping zookeepers improve habitat design and enrichment planning.
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• Reduced labor load: Automating routine monitoring tasks gives animal care teams more time to focus on hands-on enrichment, training, and medical care.
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• Remote collaboration and diagnosis: Real-time video and behavior data can be shared with off-site vets, behaviorists, or researchers, enabling collaborative care and quicker diagnoses.
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• Enhanced night-time monitoring: Infrared and low-light vision systems allow staff to monitor animals after hours or during nocturnal activity - something that's hard to do manually without disturbing the animals.

While computer vision offers many benefits with respect to animal care, there are also some challenges to consider. Here are a few key limitations and factors to keep in mind when adopting this technology:

• Species-specific behavior complexity: Some species have subtle, rare, or understudied behaviors that are difficult to accurately label and detect without extensive, high-quality training data.
  
  
• High initial setup costs: The cost of high-resolution cameras, durable weatherproof equipment, servers, and edge devices can be a significant investment, particularly for smaller zoos or conservation facilities.
  
  
• Limited coverage and blind spots: Animals often move into dens, hide behind trees or rocks, or use vertical space - areas that fixed cameras may not capture without extensive and costly multi-angle setups.
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• Limited real-time insight for complex behaviors: While simple behaviors like sleep or movement are easier to detect, nuanced social interactions or emotional states are harder for AI models to interpret accurately.

Key takeaways

Computer vision is steadily changing how animals are cared for in zoos, veterinary clinics, and reserves. Its ability to monitor movement, detect early signs of illness, and track individual animals makes it a reliable tool. While these systems can’t replace human caregivers; they can help human caregivers do their jobs more efficiently.

Looking ahead, we are likely to see more advanced uses of AI in animal health monitoring. For instance, emotion detection could help track stress or comfort levels in real time, while pose estimation can catch early signs of injury before they become visible. When integrated with smart enclosures and wearable sensors, computer vision could become part of a connected system that gives caretakers deeper insights into each animal’s well-being. 

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