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XPM: An explainable-by-design pattern-based estrus detection approach to improve resource use in dairy farms

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Abstract

A powerful automatic detection of estrus, the only period when the cow is susceptible to pregnancy, is a key driver to help farmers with reproduction management and subsequently to improve milk production resource use in dairy farms. Automatic solutions to detect both types of estrus (behavioral and silent estrus) based on the combination of affordable phenotyping data (activity, body temperature) exist, but they do not provide faithful explanations to support their alerts and in ways that farmers can understand based on the behaviors they could observe in animals. In this paper, we first propose XPM, a novel pattern-based classifier to detect both types of estrus with real-world affordable sensor data (activity, body temperature) which supports its predictions with perfectly faithful explanations. Then, we show that our approach performs better than a commercial reference in estrus detection, driven by the detection of silent estrus. Finally, we present the explainability of our solution which stems from the communication to the farmers the presence and/or absence of a limited number of patterns determinant of estrus detection, therefore reducing solution mistrust and supporting farmers' decision-making.
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Dates and versions

hal-03483109 , version 1 (16-12-2021)
hal-03483109 , version 2 (17-12-2021)

Identifiers

  • HAL Id : hal-03483109 , version 2

Cite

Issei Harada, Kevin Fauvel, Thomas Guyet, Véronique Masson, Alexandre Termier, et al.. XPM: An explainable-by-design pattern-based estrus detection approach to improve resource use in dairy farms. AAAI 2022 - 36th AAAI Conference on Artificial Intelligence, Feb 2022, Vancouver, Canada. pp.1-10. ⟨hal-03483109v2⟩
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