Animal Monitoring

Deep learning for vision-based animal monitoring to increase productivity in farming.

What is Animal Monitoring?

Animal monitoring with computer vision is an important smart farm application. Deep learning is used to monitor the health of specific livestock such as pigs or cattle.

The increasing demand for sustainable livestock products also demands new considerations in animal breeding. Systems seeking to analyze animal behavior aims to increase productivity (aggression, drinking, feeding, lameness, mounting, posture, nursing), health, and welfare of the animals and thereby influence yields and economic benefits in the industry.

Key Features of Animal Monitoring With Deep Learning

Recognition of livestock behaviors with AI vision is very powerful. However, it faces challenges such as complex scenes, illumination, occlusion, and overlapping between livestock. Novel deep learning models are able to overcome these difficulties and are adopted to recognize livestock behaviors in large-scale farms.

  • Recognition and localization of livestock such as pigs in farms with real-time algorithms using common, inexpensive surveillance cameras.
  • Real-time monitoring of entire animal groups with multiple cameras across different farms.
  • Define specific areas within camera streams (region-of-interest) to improve the recognition accuracy.
  • Edge Computer Vision applies on-device machine learning that is robust for mission-critical AI vision (offline capabilities).
  • Privacy-preserving image processing to guarantee the privacy of staff and farm workers on-site.
  • Flexible processing logic to use insights for integrating with other systems, send alerts, and create reports in real-time dashboards.

Value of Vision-Based Animal Monitoring Solutions

Automatic animal monitoring with computer vision drives numerous benefits for farms that drive productivity. The advantages of animal monitoring with computer vision range from automation and early detection to the enforcement of sustainability label guidelines.

  • Automation: Traditional manual observation of pig behavior is time-consuming, laborious, subjective, and difficult to continuously conduct in large-scale operations. Computer vision technology provides the advantage of being objective, non-invasive, and continuous.
  • Large-scale monitoring: Compared to other methods, AI vision for animal monitoring is highly scalable and less costly to implement and maintain. Contactless and non-invasive methods avoid animal stress.
  • Sustainability labels: Automatically monitor compliance to label standards. Avoid violations of label guidelines across multiple farms.
  • Productivity gains: Increased health and welfare of livestock lead to improved product quality and profitability.
  • Actionable insights: Early detection of specific conditions by monitoring the activity of pigs in real-time. Early warning system without human inspection.
Last updated
September 17, 2021
Requires Viso Suite

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