Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges
Abstract
1. Introduction
2. Sensors and Data Streams for Livestock Monitoring
3. Framing the Prediction/Decision and Validation Problem
4. Methods for Predictions/Decisions
4.1. Distribution-Free Statistical Approaches
4.2. Anomaly/Change-Point Detection
4.3. Classical Statistical Modelling
4.3.1. Modelling Usual Monitoring Data
4.3.2. Modelling Based on the Outcome of Interest
4.4. Latent Class or Variable Modelling
4.5. Machine Learning Methods
4.5.1. Basic Machine Learning Methods
4.5.2. Neural Networks
4.5.3. Application of Machine Learning in Prediction/Decision Context
4.6. Discussion of Alternative Prediction/Decision Methods
5. Validation of Predictions/Decisions
5.1. Challenges
5.2. Data Visualisation
5.3. Quantitative Assessment
5.3.1. Classification
5.3.2. Severity
5.3.3. Time Lags and Other Temporal Considerations
5.4. Cross-Validation
5.5. On-Farm Validation in Practice
5.6. Other Considerations
6. Detailed Examples and Types of Studies
6.1. Small-Scale Clinical Studies for Specific Health Issues
6.2. On-Farm and In-Field Studies
6.2.1. Dairy Farms
6.2.2. Extensively Managed Cattle and Sheep
6.2.3. Pigs and Poultry
6.2.4. Summary
6.3. High-Level Validation Studies
7. Summary and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Mathematical Details for Simulation Program
Appendix A.1. Generating Health Data
Appendix A.2. Generating Management Data
Appendix A.3. Generating Sensor Data
Appendix A.4. Parameters Used in Simulation
Parameter Name | Equation | Parameters 1 |
---|---|---|
(A1) | (0.4, 0.3, 0.5, 0.2) | |
(A2) | (0.01, 0.02, 0.03, 0.01) | |
(A3) | (2.0, 1.0, 0.5, 2.0) | |
(A3) | (4.0, 2.0, 0.5, 4.0) | |
(A3) | (−0.5, −0.1, 0.1, −0.9) | |
(A3) | (0.5, 0.1, 0.2, −0.4) | |
(A3) | (0, 10, 0, 0) | |
(A3) | (0, 30, 0, 0) | |
(A3) | (20, 10, 20, 30) | |
(A3) | (50, 40, 25, 70) | |
(A3) | (1, 0, 1, 1) | |
(A3) | (0.001, 0.001, 0.001, 0.001) | |
(A5) | (0, 0, 0, 0) |
Parameter Name | Equation | Parameters 1 |
---|---|---|
(A6) | (0, 0, 0, 0) | |
(A6) | (0.5, 0.7, 0.7, 0.9) | |
(A6) | (10, 5, 1, 4) | |
(A6) | (5, 0, 0, 0) | |
(A6) | (1, 1, 1, 1) | |
(A6) | ((1, 1, 7), (1, 1, 7), (1, 1, 1), (1, 1, 1)) 2 | |
(A7) | (10, 0, 5, 10) |
Parameter Name | Equation | Parameters 1 |
---|---|---|
(A8) | (1, 1, 1) | |
(A9) | (0.01, 0.01, 0.01) | |
(A10) | (1, 20, 2) | |
(A10) | (2, 20, 8) | |
(A10) | (0.8, −0.1, 0.8) | |
(A10) | (0.9, 0.1, 0.9) | |
(A10) | (20, 20, 0) | |
(A10) | (30, 30, 0) | |
(A10) | (30, 30, 50) | |
(A10) | (50, 50, 60) | |
(A10) | (0, 1, 1) | |
(A10) | (−1, 1, −1) | |
(A10) | (0.001, 0.001, 0.001) | |
(A11) | (0.5, 1.0, 1.0) |
Parameter Name | Equation | Parameters 1 |
---|---|---|
(A14) | (200, 100) | |
(A14) | (10, 20) | |
(A14) | (200, 40) | |
(A14) | (10, 5) | |
(A14) | (200, 30) | |
(A14) | (10, 5) | |
(A14) | (200, 10) |
Parameter Name | Equation | Parameters 1 |
---|---|---|
(A15) | ((0, 0, 0, 0), (0, 0, 0, 0)) | |
(A15) | ((−2, −2, 2, −2), (−1, −1, 0, −1)) | |
(A15) | ((4, 4, 4, 4), (16, 16, 16, 16)) | |
(A15) | ((0, 0, 0, 0), (5, 0, 0, 0)) | |
(A15) | ((1, 1, 1, 1), (0, 1, 1, 1)) |
Parameter Name | Equation | Parameters 1 |
---|---|---|
(A16) | ((0, 0, 0), (0, 0, 0)) | |
(A16) | ((1, 1, 1), (0.25, 0.25, 0.25)) | |
(A16) | ((1, 1, 1), (1, 1, 1)) | |
(A16) | ((0, 0, 0), (0, 0, 10)) | |
(A16) | ((1, 1, 1), (1, 1, 0)) |
Appendix A.5. Code Used
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Name | Species Being Monitored 1 | What Is Being Measured | Sensor Technology | Purpose of Monitoring | Comments |
---|---|---|---|---|---|
1 Individual intakes | Dairy Cows, Beef Cattle, Calves, Sheep | Individual feeding and drinking behaviour, amounts if technology allows | Sensors at feeders/drinkers that record individual RFID tags. More advanced systems that also record feed or drink taken at each bout. | Managing Nutrition and Production; Detecting Health and Welfare Problems of individuals | |
2 Group intakes | Dairy Cows, Beef Cattle, Calves, Pigs, Poultry, Goats, Sheep | Group feeding and drinking including amounts | Automatic livestock feeders and drinkers | Managing group Nutrition and Production; Detecting Health and Welfare Problems in groups | |
3 Individual weights—identified individuals | Dairy Cows, Beef Cattle, Pigs, Sheep | Individual Live weights | Walk-over weighers that record individual RFID tags. | Managing Nutrition and Production; Detecting Health and Welfare Problems of individuals | Walk-over weighers placed to maximise the number of readings (e.g., on way in/out of milking parlour) |
4 Individual weights—unidentified individuals | Pigs, Sheep | Live weights measured per individual but individuals not identified | Walk-over weighers (e.g., at races, or in pens) | Managing Nutrition and Production; Detecting Health and Welfare Problems in groups/of individuals | Can be used to sort into different feeding areas using marking and/or gate system. For pigs in pens, they can be placed between loafing and feeding areas or separated off if unwell. |
5 Estimated weights—groups/ unidentified individuals | Poultry—Broilers, Turkeys | Live weight plus number on plate hence average liv weights; individuals not identified | Weighing Plates/Platforms for individuals/groups | Managing group Nutrition and Production; Detecting Health and Welfare Problems in groups | This is a sampling of weights in the flock. Could give 1000 s of weight measurements per day. Some platforms only measure one bird at a time whilst some measure multiple birds. |
6 Milk parlour data | Dairy Cows | Milk yield, milking duration, peak flow; milk quality, Somatic Cell Count (SCC); position in parlour/milker | Automatic milking systems plus manual sampling | Managing Nutrition and Production; Detecting Health and Welfare Problems of individuals | Milk quality and SCC measures may not be available in real time but could be sampled regularly (e.g., once per day or week). |
7 Milk bulk lab data | Dairy Cows | Somatic Cell Count (SCC), Milk quality | Milk bulk sampling—manual sampling | Managing group Nutrition and Production; Detecting Health and Welfare Problems in groups | Milk quality and SCC measures may not be available in real time but could be sampled regularly (e.g., once per day or week). |
8 Milk bulk other data | Dairy Cows | Temperature, Volume, Stirring | Milk bulk sampling—various sensors | Managing group Milk Production and Processing | Real-time monitoring of physical attributes of milk in bulk tanks is available |
9 Movement—acceleration | Dairy Cows, Beef Cattle, Pigs, Sheep, Goat | Behaviour (e.g., activity or time budgets in different classes: lying/standing, grazing/not, rumination, … or raw acceleration in x, y, and z directions) | Accelerometer | Detecting Heat, Calving/Lambing/Farrowing, Health and Welfare Problems | Not usually used on pigs on real farms. For sheep cheaper options needed. For grazing animals, they are often removed at intervals for data download and recharging. Can give raw accelerometer data but sometimes measures are derived only (e.g., behaviour). |
10 Movement—gait | Dairy Cows, Beef Cattle, Pigs, Sheep, Goat | Behaviour (step count), other gait measurements | Pedometer | Detecting Heat, Calving/Lambing/Farrowing, Health and Welfare Problems | Less advanced than accelerometer; some just measure step count but others take measurements that can be used to detect lameness |
11 Location | Dairy Cows, Beef Cattle, Pigs, Sheep, Goat | Location and behaviour | GNSS (Global navigation satellite system), GPS (global positioning system) | Managing Grazing and Production; Detecting Health and Welfare Problems of individuals | |
12 Relative location | Dairy Cows, Beef Cattle, Pigs, Sheep, Goat | Location relative to static receivers and behaviour | Proximity loggers plus static receivers | Managing Grazing and Production; Detecting Health and Welfare Problems of individuals | Locations can be estimated as well as mother-offspring distances |
13 Images—unidentified individuals | Pigs | Body condition score, liveweight | 2D Imaging from above | Managing Nutrition and Production; Detecting Health and Welfare Problems of individuals | Can be placed between loafing and feeding areas and used to sort into different feeding areas using gate system |
14 Images—identified individuals | Cows, Pigs | Body condition score, live weight, behaviour | 2D/3D Imaging from above | Managing Nutrition and Production; Detecting Health and Welfare Problems of individuals | Identifying individuals is difficult, so it is used in combination with reading RFID tags at intervals and then tracking. |
15 Images—unidentified birds | Poultry—Broilers, Turkeys | Location and behaviour; dead birds; weight estimation; | 2D Imaging from above | Managing Nutrition and Production; Detecting Health and Welfare Problems of groups | Imaging systems for poultry tend to occur at a larger scale (per individual) than those for cows and pigs. |
16 Temperature—unidentified individuals | Dairy Cows, Beef Cattle, Calves, Pigs, Poultry, Goats, Sheep | Body temperature | Thermal Imaging | Detecting Health and Welfare Problems of individuals | Can be used for detecting heat stress, and potentially fever, pain, … |
17 Temperature—identified individuals | Dairy Cows | Body temperature | Thermometer | Detecting Health and Welfare Problems of individuals | |
18 Sound—vocalisations | Dairy Cows, Beef Cattle, Calves, Pigs, Poultry, Goats, Sheep | Specific Species-Dependent Vocalisations | Acoustic Sensors | Detecting Health and Welfare Problems of Groups | These sensors are mounted in, e.g., house, but could be used outside in confined areas |
19 Sound—feeding | Cows, Sheep | Feed intake, behaviour (grazing, ruminating) | Acoustic Sensors | Managing Grazing and Production | These sensors are mounted on animals |
20 Aerial images—extensive | Available Grazing for Cows, Sheep | Quality of grazing | Remote Sensing (Satellite imaging) | Managing Grazing | |
21 Aerial images—targeted | Cows, Sheep and Available Grazing | Quality of grazing; location of groups | Camera on Drone/UAV (Unmanned Aerial Vehicle) | Managing Grazing; Detecting Health and Welfare Problems | |
22 Local environmental conditions | Livestock | Temperature, humidity, emissions (e.g., Ammonia, Methane, CO2) | Environmental sensors | Managing Health and Welfare Problems of groups; Managing emissions | Usually for housed livestock |
23 Weather outside | Livestock | Temperature, humidity, Rainfall, Windspeed, … | Weather station | Managing Health and Welfare Problems of groups | Could affect housed livestock as well as livestock kept outside |
Name | Measurement On | Timing of Sensor Measurements | Animals Are | Sensor Is | Sensor Is | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Individuals ID Known | Individuals ID Not Known | Some Individuals ID Not known | Groups or Impact on Groups | Continuous or Near-Continuous | Intermittent | Regular | Housed (Usually) | Outside/Grazing | On animal | Not on Animal | At Fixed Location 1 | Mobile | |
1 Individual intakes | ● | ● | ● | ● | ● | ● | |||||||
2 Group intakes | ● | ● | ● | ● | ● | ● | ● | ||||||
3 Individual weights—identified individuals | ● | ● | ● | ● | ● | ● | ● | ||||||
4 Individual weights—unidentified individuals | ● | ● | ● | ● | ● | ||||||||
5 Estimated weights—group/unidentified individuals | ● | ● | ● | ● | ● | ||||||||
6 Milk parlour data | ● | ● | ● | ● | ● | ● | ● | ||||||
7 Milk bulk lab data | ● | ● | ● | ● | ● | ● | ● | ||||||
8 Milk bulk other data | ● | ● | ● | ● | ● | ● | |||||||
9 Movement—acceleration | ● | ● | ● | ● | ● | ● | ● | ||||||
10 Movement—gait | ● | ● | ● | ● | ● | ● | ● | ||||||
11 Location | ● | ● | ● | ● | ● | ● | |||||||
12 Relative location | ● | ● | ● | ● | ● | ● | ● | ||||||
13 Images—unidentified individuals | ● | ● | ● | ● | ● | ● | |||||||
14 Images—identified individuals | ● | ● | ● | ● | ● | ● | |||||||
15 Images—unidentified birds | ● | ● | ● | ● | ● | ● | ● | ● | |||||
16 Temperature—unidentified individuals | ● | ● | ● | ● | ● | ● | ● | ● | |||||
17 Temperature—identified individuals | ● | ● | ● | ● | ● | ● | ● | ||||||
18 Sound—vocalisations | ● | ● | ● | ● | ● | ● | |||||||
19 Sound—feeding | ● | ● | ● | ● | ● | ● | ● | ||||||
20 Aerial images—extensive | ● | ● | ● | ● | |||||||||
21 Aerial images—targeted | ● | ● | ● | ● | ● | ● | ● | ||||||
22 Local environmental conditions | ● | ● | ● | ● | ● | ||||||||
23 Weather outside | ● | ● | ● | ● | ● |
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Brocklehurst, S.; Fang, Z.; Butler, A. Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges. Sensors 2025, 25, 5871. https://doi.org/10.3390/s25185871
Brocklehurst S, Fang Z, Butler A. Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges. Sensors. 2025; 25(18):5871. https://doi.org/10.3390/s25185871
Chicago/Turabian StyleBrocklehurst, Sarah, Zhou Fang, and Adam Butler. 2025. "Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges" Sensors 25, no. 18: 5871. https://doi.org/10.3390/s25185871
APA StyleBrocklehurst, S., Fang, Z., & Butler, A. (2025). Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges. Sensors, 25(18), 5871. https://doi.org/10.3390/s25185871