Sensor Fusion for Predicting Impaired Resilience in Pigs: Towards an Early Warning System

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Abstract

Concerns about the health and welfare of intensively farmed pigs have raised questions about their resilience in coping with environmental stressors. This study investigates the potential of various sensors and their combination in predicting factors contributing to reduced resilience in pigs, aiming to develop an early warning system. In our experiment, sensors were placed in a pig farm containing 12 pens, each pen with thirty weaned piglets. Piglets were introduced post-weaning, staying until reaching approximately 25 kilograms live weight and data will be collected during at least 4 subsequent production cycles. Sensors included water-intake sensors, climate sensors (CO2, NH3, temperature, illuminance), audio recording sensors for coughing, and video cameras. Additional data were manually collected to determine when impaired resilience occurred, like tail and ear-damage. We conducted a MANOVA test to determine how different environmental conditions can result in biting behaviour among animals. Our initial findings indicate no statistically significant relationship, potentially due to the limited sample size. Future efforts will involve incorporating more diverse sensor data to enhance the robustness of our analysis.

Original languageEnglish
Title of host publication11th European Conference on Precision Livestock Farming
EditorsDaniel Berckmans, Patrizia Tassinari, Daniele Torreggiani
PublisherEuropean Association for Precision Livestock Farming
Pages1298-1306
Number of pages9
ISBN (Electronic)9791221067361
ISBN (Print)9798331303549
Publication statusPublished - Oct 2024
Event11th European Conference on Precision Livestock Farming - Bologna, Italy
Duration: 9 Sept 202412 Sept 2024

Conference

Conference11th European Conference on Precision Livestock Farming
Country/TerritoryItaly
CityBologna
Period9/09/2412/09/24

Keywords

  • Early warning system
  • Environmental sensors
  • Precision livestock farming
  • Resilience
  • Sensor fusion

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