Accepted Papers

Fitbit for Chickens? Time Series Data Mining Can Increase the Productivity of Poultry Farms

Alireza Abdoli: University of California Riverside; Sara Alaee: University of California Riverside; Shima Imani: University of California Riverside; Amy Murillo: University of California Riverside ; Alec Gerry: UC Riverside; Leslie Hickle: FarmSense Inc; Eamonn Keogh: UC Riverside


Chickens are the most important poultry species in the world. Globally, industrial-scale production systems account for most of the poultry meat and eggs produced. The welfare of these birds matters for both ethical and economic reasons. From an ethical perspective, poultry have a sufficient degree of awareness to suffer pain if their health is poor, or deprivation if poorly housed. From an economic viewpoint, consumers increasingly value poultry welfare, so better market access can be obtained by producers who demonstrate concern for their flocks. Recent advances in sensor technology has allowed the opportunity to record behavioral patterns in chickens, and several research groups have shown that such data can be exploited to enhance chicken welfare. However, classifying chicken behaviors poses several unique challenges which are not observed in the UCR archive or other classic benchmark collections. In particular, some behaviors are manifested in the shape of the subsequences, whereas others only in more abstract features. Most algorithms only work well for one such modality. In addition, our data of interest has classes that greatly differ in duration, and are only weakly labeled, again defying the assumptions of the classic benchmark datasets. In this work, we propose a general-purpose framework to robustly learn and classify from datasets exhibiting these issues. While our experience is with fowl, the lessons we have learned may be more generally applicable to real-world datasets in other domains including manufacturing and human health.

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