Estimation of Body Surface Temperature Change of Swine Using Thermography by Machine Learning Approach

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  • MITO Misaki
    Graduate School of Systems and Information Engineering, University of Tsukuba
  • KAWAGISHI Takuji
    Graduate School of Systems and Information Engineering, University of Tsukuba
  • MIZUTANI Koichi
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • ZEMPO Keiichi
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • WAKATSUKI Naoto
    Graduate School of Systems and Information Engineering, University of Tsukuba Acoustic Laboratory, Faculty of Engineering, Information and Systems, Division of Engineering Interaction Technologies, University of Tsukuba
  • KUBOTA Yoshifumi
    Swine Research Sec., Central Research Institute for Feed and Livestock, National Federation of Agricultural Cooperative Associations

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Other Title
  • サーモグラフィを用いる豚の体表面温度変化の機械学習アプローチによる推定
  • サーモグラフィ オ モチイル ブタ ノ タイヒョウメン オンド ヘンカ ノ キカイ ガクシュウ アプローチ ニ ヨル スイテイ

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Abstract

It is a big loss if overlooks swine estrus in breeding work in swine farm. Whether breeding swine are in estrus is a large work load for workers since it is needed to observe carefully many swine based on their experience in breeding work in swine farm. Previous research has shown that whether breeding swine are in estrus can be checked by measuring vulvar surface temperature change using thermography as a quantitative method. However, it was reported that temperature is easy to be changed by infl uence of external environment such as ambient temperature of swine. In this paper, we extract 5 external environment factors; gluteal surface temperature of swine using thermography, the temperature inside and outside swine farm. And we estimate vulvar temperature eliminated the infl uence of their factors. We performed 3 regression analysis by machine learning method, and evaluated the combination their factors and method for estimating. As a result, we could estimate vulvar temperature whose error is less, than 1.4 °C using gluteal temperature in swine, the temperature inside and around swine farm as external environment factors as explanatory variables. And we found this method can estimate temperature change by estrus correctly.

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