Monitoring animal behavior using computer vision
How often do you struggle with issues related to a lack of data on animal behavior? Most farmers do not think about how technology can influence animal welfare. In this article, we want to guide you through the computer vision solutions that will transform your animal behavior monitoring. Why computer vision? Well, out of the available tech systems, our team considers this one the most relevant to animal businesses. Surely, more technologies can benefit, yet we recommend adding computer vision-based solutions to your tech arsenal.
What is a computer vision solution for animal behavior monitoring?
In this piece, we will focus specifically on monitoring animal behavior. Why? Animal behavior monitoring is crucial for farmers as it provides valuable insights into the health, welfare, and productivity of livestock, leading to more informed decision-making and improved operational efficiency.
Here’s why it’s important and how it impacts farmers’ ROI:
- Health monitoring. By tracking animal behavior, such as eating, drinking, movement patterns, and social interactions, farmers can detect early signs of illness or stress. This allows for timely interventions, reducing the need for expensive treatments and minimizing losses due to disease outbreaks.
- Improved reproduction management. Behavioral monitoring can identify when animals are in heat, optimizing breeding schedules. This leads to better reproductive success rates and ensures higher yields in milk, meat, or offspring, directly influencing profitability.
- Feed efficiency. Monitoring feeding behavior helps optimize nutrition by ensuring that each animal is getting the right amount of food. This reduces feed waste and ensures the growth and health of the animals, contributing to higher productivity and lower feed costs.
- Stress and welfare management. Stress in animals can reduce productivity, such as lower milk yield in dairy cows or weight gain in livestock. Monitoring stress levels through behavior analysis allows farmers to make environmental or management changes that improve welfare, resulting in healthier, more productive animals.
- Preventative maintenance. Early detection of behavioral changes can signal potential problems in equipment, feeding systems, or the living environment. By addressing these issues proactively, farmers reduce operational disruptions, ensuring a steady production flow.
- Data-driven decision-making. Behavioral monitoring provides real-time data that can be analyzed to fine-tune farm management practices. This increases efficiency, reduces waste, and leads to more precise resource allocation, which lowers operational costs.
Influence on ROI:
- Reduced veterinary and treatment costs by preventing illness.
- Higher productivity due to optimized breeding, feeding, and welfare practices.
- Better resource allocation, leading to cost savings on feed and labor.
- Increased product quality (e.g., healthier animals produce higher-quality meat or milk), which can command higher prices in the market.
How does computer vision for monitoring animal behavior work?
Let’s talk about the details that matter most, the tech side of the computer vision-based solution for monitoring animal behavior. We will consider hardware and software elements here. Software is Qaltivate’s specialty, and when it comes to agriculture solutions, we can design the software for you and select the right hardware that will benefit your business best. So, what do we need to design such a solution:
Hardware
Cameras. High-resolution cameras are essential for capturing detailed video footage of animal behavior. These could include:
- IP cameras. For remote monitoring, often used in large-scale farming operations.
- Thermal cameras. To detect temperature variations, which can indicate stress, illness, or reproductive readiness.
- 3D cameras. For depth perception, useful in tracking movement patterns and understanding spatial behavior.
Here is a review of some cameras that could be used for computer vision solutions for monitoring animal behavior:
Camera Model | Key Features | Best For |
Basler ace Series | High-resolution sensors (up to 12.2 MP), GigE and USB 3.0 interfaces, Robust software support with Basler Pylon SDK | High-resolution, scalable vision tasks in various environmental conditions |
FLIR Blackfly S | Global shutter with resolutions up to 20 MP, USB 3, GigE, and PoE options, On-camera processing for computer vision tasks | High-speed, real-time animal monitoring in dynamic environments |
Intel RealSense Depth Camera D455 | High-accuracy depth sensing up to 10 meters, Stereo image sensors with a wide field of view, High frame rate (90 fps) | Depth perception and spatial monitoring in farm environments |
Allied Vision Alvium Series | Wide range of resolutions (VGA to 12 MP), CSI-2, USB 3, and GigE interfaces, Low power consumption | Embedded vision applications where low power and local processing are crucial |
Hikvision Machine Vision Camera | Resolutions up to 31 MP, Global shutter and GigE/USB 3.0 support, Low-light imaging capabilities | Cost-effective, high-performance monitoring in challenging light and motion environments |
Edge Devices. For farms with limited connectivity or large data volumes, edge computing devices (e.g., Nvidia Jetson, Raspberry Pi) can process data locally, reducing latency and bandwidth requirements by performing some analysis close to the source.
Sensors. Complementary sensors like motion sensors, RFID tags, or accelerometers can provide additional data, such as movement frequency, body temperature, and location tracking, enhancing the overall behavioral analysis.
Sensor Type | Purpose | Benefits | Use Case |
Motion Sensors (Accelerometers and Gyroscopes) | Detect movement and orientation of animals | Monitor fine movements, activity levels, even when animals are out of camera range | Detect abnormal movements like limping or excessive restlessness |
RFID Tags | Allow identification and tracking of individual animals | Precise identification, tracking specific animals in large herds | Track specific animals during feeding or behavioral studies |
Temperature Sensors | Measure the body temperature of animals | Monitor health indicators, early detection of disease or stress | Detect infections by monitoring temperature spikes in livestock |
Microphones (Sound Sensors) | Capture animal vocalizations to indicate stress or health | Detect stress, social interactions, or health issues through sound | Monitor pig vocalizations or detect respiratory issues in poultry |
Pressure or Weight Sensors | Track weight changes or detect interactions with objects | Monitor feeding behavior, detect rapid weight changes or unusual interactions | Track time spent by cows at feeding stations or stalls |
GPS Sensors | Track location and movement patterns in large areas | Monitor grazing patterns and detect abnormal movements in free-range environments | Track cattle movements in free-range environments to optimize grazing |
Infrared (IR) and Thermal Sensors | Detect heat emitted by animals and monitor temperature changes | Detect heat stress, night-time monitoring, and health-related temperature issues | Monitor animal temperature in large poultry farms to prevent overheating |
Humidity and Environmental Sensors | Monitor environmental conditions like humidity and air quality | Provide context to behavioral data by monitoring environmental factors | Detect high humidity leading to respiratory issues and correlate with behavior changes |
Lidar Sensors | Provide precise 3D spatial information about animal movement | Enhance spatial tracking with 3D data, solving occlusion issues | Track movements and interactions in crowded environments with occlusion |
Proximity Sensors | Detect the presence of animals near specific areas | Track feeding, drinking times or animal movement through gates or enclosures | Detect animal entry/exit from enclosures or feeding zones |
Connectivity infrastructure. Reliable internet or local network connections are needed for transmitting data from the cameras and sensors to the processing units. For farms in remote areas, this could involve Wi-Fi, LTE/5G, or LoRaWAN networks.
Cloud servers. If edge devices aren’t enough to handle all the data processing, cloud infrastructure such as AWS, Google Cloud, or Microsoft Azure is needed for data storage, analysis, and backup.
Software
Computer vision algorithms. The core of the system, these algorithms detect and classify animal behavior. They typically involve:
- Object detection models (e.g., YOLO, SSD) to identify animals and their postures.
- Pose estimation to track specific movements or behaviors, such as walking, eating, or lying down.
- Behavioral pattern recognition using machine learning to understand normal vs. abnormal activity.
Machine learning/deep learning Models. To improve accuracy over time, machine learning models (e.g., using TensorFlow, PyTorch) are trained to recognize specific behaviors. These models require continuous training based on video data and sensor input.
Data analytics and visualization platforms. Farmers need user-friendly dashboards to interpret data. Software tools like Power BI, Grafana, or custom-built dashboards display key performance indicators (KPIs), alerts, and trends. Real-time notifications and reports can be delivered via mobile apps or desktop interfaces.
Cloud processing and storage. The captured video data is often uploaded to the cloud for storage and advanced analysis. Tools like AWS S3 (for storage) and AWS Lambda (for running code on demand) can be used to process this data in scalable environments.
Integration with farm management systems (FMS). The behavior data can be integrated with existing farm management software to automate decision-making, optimize feeding schedules, and generate reports on animal health and productivity.
Key considerations for the design of computer vision-based animal monitoring solution
Designing software for cameras used in computer vision to monitor animal behavior comes with several challenges. Here are some common problems that can arise:
- Lighting variability
Different lighting conditions (day/night, indoors/outdoors, shadows, etc.) can cause inconsistent image quality, making it difficult for the software to accurately interpret animal behavior. So, use cameras with high dynamic range (HDR) and design software that can adapt to varying lighting conditions through automatic brightness/contrast adjustments or filtering techniques.
2. Occlusion
Animals often move in groups or interact with objects, which can result in one animal partially or completely blocking the view of another, causing difficulties in behavior tracking. Implement multi-camera systems or use 3D/depth-sensing cameras to capture different perspectives. Develop algorithms that can estimate occluded movements based on previous frames.
3. Motion blur
Fast-moving animals can cause motion blur, which makes it challenging for the software to detect specific behaviors or track the animal accurately. Use high-frame-rate cameras and incorporate image stabilization or motion compensation algorithms to minimize blur during fast movements.
4. Background noise
Farms or outdoor environments can have noisy backgrounds (e.g., wind, moving trees, other animals), which can interfere with behavior detection. Implement background subtraction techniques and use robust object detection models that can differentiate between the animals and environmental noise.
5. Environmental conditions
Cameras used in outdoor environments are exposed to harsh conditions like dust, rain, or extreme temperatures, which can affect image quality or even damage the hardware. Use rugged, weatherproof cameras, and design software that compensates for image quality degradation in challenging conditions through filtering or pre-processing techniques.
6. Complex movements
Animals exhibit a wide range of behaviors and complex movements that can be difficult for traditional computer vision algorithms to interpret. Leverage machine learning or deep learning techniques to develop models capable of learning complex behaviors from large datasets. Fine-tune models with specific datasets that capture the nuances of animal behavior.
7. Real-time processing
Processing video feeds in real-time, especially with large numbers of animals, requires significant computational power, which can lead to performance bottlenecks. Implement edge computing to perform some of the processing on local devices. Use optimized algorithms and hardware acceleration (e.g., GPUs, TPUs) to ensure real-time performance.
8. Data storage and bandwidth
Continuous video recording generates massive amounts of data, leading to storage and bandwidth challenges, particularly in remote locations with limited connectivity. Use data compression techniques and only store or transmit critical data (e.g., anomalies). Implement edge processing to reduce the volume of data sent to the cloud.
9. Animal variability
Different species, breeds, or even individual animals exhibit unique behaviors, making it difficult to create one-size-fits-all models for behavior monitoring. Use adaptive machine learning models that can be trained on data specific to each farm or species. Provide customization options in the software to tailor monitoring to specific animal types.
10. False positives and false negatives
Computer vision systems can generate false positives (detecting behavior that didn’t occur) or false negatives (missing behavior that did occur), especially in noisy environments or complex situations. Continuously refine and update the machine learning models with more accurate datasets. Implement redundancy in behavior detection, such as combining visual data with sensor data (e.g., accelerometers or RFID).
Ready to design your computer vision-based animal monitoring solution?
At Qaltivate, we bring deep expertise and hands-on experience in AgTech, making us the ideal partner for designing advanced solutions like animal behavior monitoring systems. Our proven track record in developing tailored technologies for the agriculture industry ensures that we can create robust, scalable, and efficient solutions that meet the unique needs of your farm operations. We invite you to explore more about AgTech innovations, RFID tracking, and farming technology by tuning into our podcast, Digital Ag Global. This platform provides valuable insights and answers to many of your questions, with opportunities to engage with us live or anytime through our page. Join the conversation and stay ahead in the rapidly evolving world of AgTech!