Hamilton et al., 2019 - Google Patents
Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensorsHamilton et al., 2019
View HTML- Document ID
- 18290101181196570729
- Author
- Hamilton A
- Davison C
- Tachtatzis C
- Andonovic I
- Michie C
- Ferguson H
- Somerville L
- Jonsson N
- Publication year
- Publication venue
- Sensors
External Links
Snippet
The reticuloruminal function is central to the digestive efficiency in ruminants. For cattle, collar-and ear tag-based accelerometer monitors have been developed to assess the time spent ruminating on an individual animal. Cattle that are ill feed less and so ruminate less …
- 206010027387 Merycism 0 title abstract description 72
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hamilton et al. | Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensors | |
Cockburn | Application and prospective discussion of machine learning for the management of dairy farms | |
Mansbridge et al. | Feature selection and comparison of machine learning algorithms in classification of grazing and rumination behaviour in sheep | |
Pandey et al. | Behavioral monitoring tool for pig farmers: Ear tag sensors, machine intelligence, and technology adoption roadmap | |
Carslake et al. | Machine learning algorithms to classify and quantify multiple behaviours in dairy calves using a sensor: Moving beyond classification in precision livestock | |
Lee et al. | Wearable wireless biosensor technology for monitoring cattle: A review | |
Chambers et al. | Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation | |
Augustine et al. | Assessing herbivore foraging behavior with GPS collars in a semiarid grassland | |
Barwick et al. | Identifying sheep activity from tri-axial acceleration signals using a moving window classification model | |
Cabezas et al. | Analysis of accelerometer and GPS data for cattle behaviour identification and anomalous events detection | |
Racewicz et al. | Welfare health and productivity in commercial pig herds | |
Mahfuz et al. | Applications of smart technology as a sustainable strategy in modern swine farming | |
Balasso et al. | Machine learning to detect posture and behavior in dairy cows: Information from an accelerometer on the animal’s left flank | |
Fogarty et al. | Developing a simulated online model that integrates GNSS, accelerometer and weather data to detect parturition events in grazing sheep: a machine learning approach | |
Adenuga et al. | Economic viability of adoption of automated oestrus detection technologies on dairy farms: A review | |
Davison et al. | Detecting heat stress in dairy cattle using neck-mounted activity collars | |
Iqbal et al. | Validation of an accelerometer sensor-based collar for monitoring grazing and rumination behaviours in grazing dairy cows | |
Schmeling et al. | Training and validating a machine learning model for the sensor-based monitoring of lying behavior in dairy cows on pasture and in the barn | |
Colpoys et al. | Evaluation of the FitBark activity monitor for measuring physical activity in dogs | |
Leso et al. | Validation of a commercial collar-based sensor for monitoring eating and ruminating behaviour of dairy cows | |
Kapun et al. | Case study on recording pigs’ daily activity patterns with a uhf-rfid system | |
Bloch et al. | Development and analysis of a CNN-and transfer-learning-based classification model for automated dairy cow feeding behavior recognition from accelerometer data | |
Watanabe et al. | Strategy to predict high and low frequency behaviors using triaxial accelerometers in grazing of beef cattle | |
Li et al. | Classification and analysis of multiple cattle unitary behaviors and movements based on machine learning methods | |
Li et al. | Validation and use of the rumiwatch noseband sensor for monitoring grazing behaviours of lactating dairy cows |