25 Computer Vision and Camera Technologies

25.1 Overview

This chapter explores camera-based phenotyping using 2D and 3D imaging and computer vision techniques.

25.2 Learning Objectives

By the end of this chapter, you should be able to:

Describe types of cameras used in livestock monitoring (2D, 3D, thermal)
Explain how computer vision is used for body condition scoring and weight estimation
Understand the role of machine learning in video analysis
Identify applications of camera-based phenotyping for genetic evaluation
Recognize challenges in deploying computer vision on commercial farms

25.3 Introduction to Computer Vision in Livestock

Chapter Status

This chapter is currently under development.

Definition: Using cameras and image analysis to extract phenotypic information

Advantages: - Non-invasive - High-throughput - Automated

Types of Cameras: - 2D cameras (RGB) - 3D cameras (depth sensors, stereo cameras, LiDAR) - Thermal cameras

25.4 2D Image Analysis

25.4.1 Applications

Body condition scoring (BCS): Automated scoring using images of animals
Activity and behavior: Posture classification, social interactions
Lameness detection: Gait analysis from video
Identification: Facial recognition, coat pattern identification

25.4.2 Computer Vision Techniques

Object detection (locate animals in images)
Image segmentation (separate animal from background)
Feature extraction (body shape, posture)
Deep learning (convolutional neural networks)

25.4.3 Commercial Systems

DeLaval body condition scoring
Cainthus (behavior monitoring)

25.5 3D Imaging and Depth Sensors

25.5.1 Technologies

Time-of-flight cameras
Structured light
Stereo vision
LiDAR

25.5.2 Applications

Body weight estimation: 3D body volume correlates with weight
Body composition: Estimate muscle and fat distribution
Growth monitoring: Automated weight tracking without handling animals
Conformation traits: Measure body dimensions (height, length, chest girth)

25.5.3 Accuracy

High correlation with manual measurements (r > 0.90 for weight in many systems)

25.6 Thermal Imaging

25.6.1 Applications

Health monitoring: Elevated body temperature indicates fever or inflammation
Heat stress detection: Monitor heat load in animals
Mastitis detection: Udder temperature changes

25.6.2 Challenges

Environmental factors affect measurements (ambient temperature, humidity)

25.7 Video Analysis for Behavior

25.7.1 Behaviors Tracked

Eating and drinking: Time spent at feeder/drinker, feeding rate
Social interactions: Aggression, mounting, grooming
Lying and standing behavior: Posture classification
Gait analysis: Lameness scoring from video
Farrowing/calving monitoring: Detect parturition events

25.7.2 Machine Learning

Train models to classify behaviors from video

25.8 Computer Vision for Genetic Evaluation

25.8.1 Novel Phenotypes

Body composition traits: 3D imaging provides accurate carcass predictions
Feed efficiency: Video analysis of feeding behavior
Health traits: Early disease detection (gait, posture, activity)
Conformation: Automated measurement of body dimensions
Welfare traits: Behavior-based indicators (fear response, social stress)

25.9 Challenges

Environmental variability: Lighting, dust, occlusions
Computational requirements: Processing large video datasets
Model training: Requires labeled data (ground truth)
Generalization: Models trained on one farm may not work on another
Cost: High-resolution cameras, computing infrastructure

25.10 Future Directions

Integration with genomic data (genotype-by-environment, resilience)
Real-time alerts for health and welfare issues
Scaling to commercial farms
Multi-modal sensing (combine video, wearables, environmental sensors)

25.11 Summary

Computer vision enables non-invasive, high-throughput phenotyping that provides novel traits for genetic selection.

25.12 Key Points

3D imaging accurately estimates body weight and composition
Machine learning is critical for analyzing video and image data
Camera-based phenotyping provides novel traits for genetic selection
Integration with breeding programs is expanding rapidly

# Computer Vision and Camera Technologies {#sec-computer-vision} ## Overview This chapter explores camera-based phenotyping using 2D and 3D imaging and computer vision techniques. ## Learning Objectives By the end of this chapter, you should be able to: 1. Describe types of cameras used in livestock monitoring (2D, 3D, thermal) 2. Explain how computer vision is used for body condition scoring and weight estimation 3. Understand the role of machine learning in video analysis 4. Identify applications of camera-based phenotyping for genetic evaluation 5. Recognize challenges in deploying computer vision on commercial farms ## Introduction to Computer Vision in Livestock ::: {.callout-note} ## Chapter Status This chapter is currently under development. ::: **Definition**: Using cameras and image analysis to extract phenotypic information **Advantages**: - Non-invasive - High-throughput - Automated **Types of Cameras**: - 2D cameras (RGB) - 3D cameras (depth sensors, stereo cameras, LiDAR) - Thermal cameras ## 2D Image Analysis ### Applications 1. **Body condition scoring (BCS)**: Automated scoring using images of animals 2. **Activity and behavior**: Posture classification, social interactions 3. **Lameness detection**: Gait analysis from video 4. **Identification**: Facial recognition, coat pattern identification ### Computer Vision Techniques - Object detection (locate animals in images) - Image segmentation (separate animal from background) - Feature extraction (body shape, posture) - Deep learning (convolutional neural networks) ### Commercial Systems - DeLaval body condition scoring - Cainthus (behavior monitoring) ## 3D Imaging and Depth Sensors ### Technologies - Time-of-flight cameras - Structured light - Stereo vision - LiDAR ### Applications 1. **Body weight estimation**: 3D body volume correlates with weight 2. **Body composition**: Estimate muscle and fat distribution 3. **Growth monitoring**: Automated weight tracking without handling animals 4. **Conformation traits**: Measure body dimensions (height, length, chest girth) ### Accuracy High correlation with manual measurements (r > 0.90 for weight in many systems) ## Thermal Imaging ### Applications 1. **Health monitoring**: Elevated body temperature indicates fever or inflammation 2. **Heat stress detection**: Monitor heat load in animals 3. **Mastitis detection**: Udder temperature changes ### Challenges Environmental factors affect measurements (ambient temperature, humidity) ## Video Analysis for Behavior ### Behaviors Tracked - Eating and drinking: Time spent at feeder/drinker, feeding rate - Social interactions: Aggression, mounting, grooming - Lying and standing behavior: Posture classification - Gait analysis: Lameness scoring from video - Farrowing/calving monitoring: Detect parturition events ### Machine Learning Train models to classify behaviors from video ## Computer Vision for Genetic Evaluation ### Novel Phenotypes 1. **Body composition traits**: 3D imaging provides accurate carcass predictions 2. **Feed efficiency**: Video analysis of feeding behavior 3. **Health traits**: Early disease detection (gait, posture, activity) 4. **Conformation**: Automated measurement of body dimensions 5. **Welfare traits**: Behavior-based indicators (fear response, social stress) ## Challenges - **Environmental variability**: Lighting, dust, occlusions - **Computational requirements**: Processing large video datasets - **Model training**: Requires labeled data (ground truth) - **Generalization**: Models trained on one farm may not work on another - **Cost**: High-resolution cameras, computing infrastructure ## Future Directions - Integration with genomic data (genotype-by-environment, resilience) - Real-time alerts for health and welfare issues - Scaling to commercial farms - Multi-modal sensing (combine video, wearables, environmental sensors) ## Summary Computer vision enables non-invasive, high-throughput phenotyping that provides novel traits for genetic selection. ## Key Points - 3D imaging accurately estimates body weight and composition - Machine learning is critical for analyzing video and image data - Camera-based phenotyping provides novel traits for genetic selection - Integration with breeding programs is expanding rapidly