Describing Behavior Sequences of Fattening Pigs Using Process Mining on Video Data and Automated Pig Behavior Recognition

Andreas Melfsen (), Arvid Lepsien, Jan Bosselmann, Agnes Koschmider and Eberhard Hartung
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Andreas Melfsen: Institute of Agricultural Engineering, Faculty of Agricultural and Nutritional Sciences, Kiel University, 24118 Kiel, Germany
Arvid Lepsien: Department of Computer Science, Faculty of Engineering, Kiel University, 24118 Kiel, Germany
Jan Bosselmann: Department of Computer Science, Faculty of Engineering, Kiel University, 24118 Kiel, Germany
Agnes Koschmider: Business & Information Systems Engineering, Faculty of Law, Business and Economics, University of Bayreuth, 95447 Bayreuth, Germany
Eberhard Hartung: Institute of Agricultural Engineering, Faculty of Agricultural and Nutritional Sciences, Kiel University, 24118 Kiel, Germany

Agriculture, 2023, vol. 13, issue 8, 1-20

Abstract: This study aimed to demonstrate the application of process mining on video data of pigs, facilitating the analysis of behavioral patterns. Video data were collected over a period of 5 days from a pig pen in a mechanically ventilated barn and used for analysis. The approach in this study relies on a series of individual steps to allow process mining on this data set. These steps include object detection and tracking, spatiotemporal activity recognition in video data, and process model analysis. Each step gives insights into pig behavior at different time points and locations within the pen, offering increasing levels of detail to describe typical pig behavior up to process models reflecting different behavior sequences for clustered datasets. Our data-driven approach proves suitable for the comprehensive analysis of behavioral sequences in conventional pig farming.

Keywords: behavior sequences; process mining; AI video analysis; fattening pigs; functional areas (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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