A5020824924- Animal Behavior and Welfare Studies
- Effects of Environmental Stressors on Livestock
- Food Supply Chain Traceability
- Animal Nutrition and Physiology
- Animal Disease Management and Epidemiology
- Agriculture and Farm Safety
- Milk Quality and Mastitis in Dairy Cows
- Genetic and phenotypic traits in livestock
- Human-Animal Interaction Studies
- Agricultural Systems and Practices
- Smart Agriculture and AI
- Agriculture Sustainability and Environmental Impact
- Livestock and Poultry Management
- Agriculture and Rural Development Research
- Meat and Animal Product Quality
- Sustainable Agricultural Systems Analysis
- Anomaly Detection Techniques and Applications
- Microbial infections and disease research
- Animal Nutrition and Health
- Wildlife Ecology and Conservation
- Agricultural Innovations and Practices
- Gait Recognition and Analysis
- Primate Behavior and Ecology
- Remote Sensing and Land Use
- Agriculture and Biological Studies
KU Leuven
2011-2018
Agricultural Research Organization
2012-2015
Institute of Agricultural Engineering
2015
The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on commercial dairy farm in Belgium. Human locomotion scoring used as reference model development evaluation. Cow behaviour performance measured with existing sensors that already present at farm. A prototype three-dimensional-based video...
Abstract Lameness is considered a major problem in dairy production. commonly detected with locomotion scores assigned to cows under farm conditions, but raters are often trained and assessed for reliability agreement by using video recordings. The aim of this study was evaluate intra- inter-rater experienced inexperienced scoring performed live from video, calculate the influence method observation (live or video) on probability classifying cow as lame. Using five-level score, were scored...
<abstract> <bold>Abstract.</bold> One of the objectives Precision Livestock Farming (PLF) is to develop on-line tools for fully automatic and continuous monitoring farm animals. PLF consists measuring animal variables, modeling these data select information calculate specific parameters, then apply in real time controlling purposes. Computer vision widely applied PLF, as well image analysis techniques that have been used e.g., automatically detect lameness dairy cows. Therefore, performance...
<abstract> <bold>Abstract.</bold> The objective of this study was to implement a computer vision system for automatic monitoring animal based measures relevant lameness detection in commercial dairy farm. implementation procedure comprised the following steps: (1) start and stop video recordings, (2) identification cow video, (3) processing including filtering good quality images calculation back posture parameters used classifying cows as lame or not lame. After implementation, performance...
<abstract> <bold>Abstract.</bold> In this study, a new computer vision technique to automatically detect lameness in dairy cows was evaluated. A 3D camera system used extract the back posture of animals from top view perspective fully automatic way. Four parameters describe curvature were by decision tree classify lame and not cows. The experiment conducted commercial Israeli farm. classification performance algorithm evaluated against visual locomotion scores given an expert veterinary....