תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםThe 57th Conference of The Zoological Society of Israel
Well-being, or quality of life, is difficult to measure, with current approaches relying on directly asking people to evaluate their happiness level. This approach necessitates respondents' ability to express themselves adequately, and therefore obviously excludes those who cannot do so, such as animals. Yet, just as every human being has different needs depending on his character, so do animals. Therefore, to measure the quality of life, we must consider each unique personality and its individual needs. Here, we present an automated tracking system that can assess cows' welfare individually, objectively, and automatically by tracking their behavior. Our primary motivation for working with cows was to improve their welfare; yet, cows also proved to be great animal models for studying behavior: they are large mammals (can carry relatively heavy equipment), have unique fur patterns (easier to video track), very social (and human-friendly), and are readily available in farms. To study the cows, we set-up a system that tracks each cow's location and behavior in realtime using electronic tags and an array of cameras. We use machine learning to automatically classify complex-behaviors such as agonistic interactions, the hierarchical structure of a group, and more. We compare behavioral data with physiological parameters such as Cortisol levels and milk composition to link behavior with welfare. Our initial results show the differences in each cow's characteristics expressed in its walking pattern, preferences for areas, facilities, and the interactions it makes with other cows. The use of behavioral tracking could be a tool for providing real-time alerts on various acute conditions. This new approach can bring about a fundamental change in the way we examine animal well-being.
The 57th Conference of The Zoological Society of Israel
Well-being, or quality of life, is difficult to measure, with current approaches relying on directly asking people to evaluate their happiness level. This approach necessitates respondents' ability to express themselves adequately, and therefore obviously excludes those who cannot do so, such as animals. Yet, just as every human being has different needs depending on his character, so do animals. Therefore, to measure the quality of life, we must consider each unique personality and its individual needs. Here, we present an automated tracking system that can assess cows' welfare individually, objectively, and automatically by tracking their behavior. Our primary motivation for working with cows was to improve their welfare; yet, cows also proved to be great animal models for studying behavior: they are large mammals (can carry relatively heavy equipment), have unique fur patterns (easier to video track), very social (and human-friendly), and are readily available in farms. To study the cows, we set-up a system that tracks each cow's location and behavior in realtime using electronic tags and an array of cameras. We use machine learning to automatically classify complex-behaviors such as agonistic interactions, the hierarchical structure of a group, and more. We compare behavioral data with physiological parameters such as Cortisol levels and milk composition to link behavior with welfare. Our initial results show the differences in each cow's characteristics expressed in its walking pattern, preferences for areas, facilities, and the interactions it makes with other cows. The use of behavioral tracking could be a tool for providing real-time alerts on various acute conditions. This new approach can bring about a fundamental change in the way we examine animal well-being.
