The main aim of the research
This study continues the previous study on recognising complex forms of movement in ruminants using the RumiWatch noseband, pedometer, and artificial intelligence analysis software. The preliminary model experiment was showing successful and promising results. Therefore, this research’s primary goal is to enrich the results of the previous study by improving the existing AI framework and involving more subject animals. The long-term goal of the research is to make early (sub-clinical) disease detection a standard part of everyday animal husbandry practice and integrate complex movement detection into commercially available sensors.
Activities
On-farm research with different aged small groups of animals
- Observe continuously the subject animals with cameras and sensors, followed by recording annotation and comparison
- Implementing additional classifiers (AI algorithms) in the framework
- Performing cross-validation of animals of different ages and sexes
- Conducting ROC analysis
Expected results
- Recognition of complex forms of movement with a successful cross-validation
- Results that support the use of complex movement detection in performance testing of breeding animals
- More accurate motion detection by implementing additional classifiers
The implementation of the research is supported by Hungarian National Rural Network (MNVH): www.videkihalozat.eu
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