Labrecque et al., 2019 - Google Patents
Real-time individual pig tracking and behavioural metrics collection with affordable security camerasLabrecque et al., 2019
View PDF- Document ID
- 16465663265997038590
- Author
- Labrecque J
- Gouineau F
- Rivest J
- Publication year
- Publication venue
- Proceedings of the EC-PLF
External Links
Snippet
This paper presents a real-time pig tracking and behavioural metrics collection system based on affordable security cameras. The proposed approach uses machine learning to detect pigs from images and automatically classify each animal's posture at any given time …
- 241000282898 Sus scrofa 0 title abstract description 30
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K29/00—Other apparatus for animal husbandry
- A01K29/005—Monitoring or measuring activity, e.g. detecting heat or mating
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
Similar Documents
Publication | Publication Date | Title |
---|---|---|
García et al. | A systematic literature review on the use of machine learning in precision livestock farming | |
Bao et al. | Artificial intelligence in animal farming: A systematic literature review | |
Cheng et al. | Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect | |
Aydin et al. | Application of a fully automatic analysis tool to assess the activity of broiler chickens with different gait scores | |
Cornou et al. | Use of information from monitoring and decision support systems in pig production: Collection, applications and expected benefits | |
Chang et al. | Detection of rumination in cattle using an accelerometer ear-tag: A comparison of analytical methods and individual animal and generic models | |
Garcia et al. | Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis | |
KR102165891B1 (en) | Livestock data analysis-based farm health state diagnosis system | |
Tian et al. | Real-time behavioral recognition in dairy cows based on geomagnetism and acceleration information | |
Zin et al. | A general video surveillance framework for animal behavior analysis | |
Cong Phi Khanh et al. | The new design of cows' behavior classifier based on acceleration data and proposed feature set | |
Shakeel et al. | A deep learning-based cow behavior recognition scheme for improving cattle behavior modeling in smart farming | |
Brouwers et al. | Towards a novel method for detecting atypical lying down and standing up behaviors in dairy cows using accelerometers and machine learning | |
US20190302074A1 (en) | System and method for detecting enteric diseases, in particular in animals, based on odour emissions | |
Suparwito et al. | The use of animal sensor data for predicting sheep metabolisable energy intake using machine learning | |
Labrecque et al. | Real-time individual pig tracking and behavioural metrics collection with affordable security cameras | |
Yaseer et al. | A review of sensors and Machine Learning in animal farming | |
Küster et al. | Automatic behavior and posture detection of sows in loose farrowing pens based on 2D-video images | |
Veldkamp et al. | Validation of non-invasive sensor technologies to measure interaction with enrichment material in weaned fattening pigs | |
Nadeem et al. | Investigation of bovine disease and events through machine learning models | |
Bello et al. | A framework for real-time cattle monitoring using multimedia networks | |
Cai et al. | A night-time anomaly detection system of hog activities based on passive infrared detector | |
Nigade et al. | Review Paper on IOT based Cattle Health Monitoring System | |
Magana et al. | Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data | |
Montout et al. | Accurate and interpretable prediction of poor health in small ruminants with accelerometers and machine learning |