Poultry farms are an important contributor to the human food chain. Worldwide, humankind keeps an enormous number of domesticated birds (e.g. chickens) for their eggs and their meat, providing rich sources of low-fat protein. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry farms; and increasingly vocal demands for improved standards of animal welfare. Recent advances in sensing technologies and machine learning allow the possibility of automatically assessing the health of some individual birds, and employing the lessons learned to improve the welfare for all birds. This task superficially appears to be easy, given the dramatic progress in recent years in classifying human behaviors, and given that human behaviors are presumably more complex. However, as we shall demonstrate, classifying chicken behaviors poses several unique challenges, chief among which is creating a generalizable "dictionary" of behaviors from sparse and noisy data. In this work we introduce a novel time series dictionary learning algorithm that can robustly learn from weakly labeled data sources.

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Peridomestic ("backyard") chicken flocks are gaining popularity in the developed world (e.g., North America or Europe), yet little is known regarding prevalence or severity of their ectoparasites. Therefore, five birds on each of 20 properties throughout southern California were surveyed in summer for on-host (permanent) and off-host dwelling (temporary) ectoparasites. Only four premises (20%) were entirely free of ectoparasites. In declining order of prevalence (% of premises), permanent ectoparasites included six chicken louse species: Menacanthus stramineus (Nitzsch) (50%), Goniocotes gallinae (De Geer) (35%), Lipeurus caponis (L.) (20%), Menopon gallinae (L.) (15%), Menacanthus cornutus (Schömmer) (5%), and Cuclotogaster heterographus (Nitzsch) (5%). Only one flea species, Echidnophaga gallinacea (Westwood) (20%), was found. Three parasitic mite species were observed: Ornithonyssus sylviarum (Canestrini & Fanzago) (15%), Knemidocoptes mutans (Robin & Lanquetin) (10%), and Dermanyssus gallinae (De Geer) (5%). Many infestations consisted of a few to a dozen individuals per bird, but M. stramineus, G. gallinae, M. cornutus, and E. gallinacea were abundant (dozens to hundreds of individuals) on some birds, and damage by K. mutans was severe on two premises. Off-host dwelling ectoparasites were rare (D. gallinae) or absent (Cimex lectularius L., Argasidae). Parasite diversity in peridomestic flocks greatly exceeds that is routinely observed on commercial chicken flocks and highlights a need for increased biosecurity and development of ectoparasite control options for homeowners.

The northern fowl mite, Ornithonyssus sylviarum, is one of the most common and damaging ectoparasites of poultry. As an obligate blood feeding mite, the northern fowl mite can cause anaemia, slower growth, and decreased egg production of parasitized birds. However, the impact of mites or other ectoparasites on hen behaviour or welfare is not well studied. Here, we use activity sensors (three-axis accelerometers) affixed to individual birds to continuously record hen movement before, during, and after infestation with mites. Movements recorded by sensors were identified to specific bird behaviours through a previously trained algorithm, with frequency of these behaviours recorded for individual birds. Hen welfare was also determined before, during, and after mite infestation of hens using animal-based welfare metrics. Northern fowl mites significantly increased hen preening behaviour and resulted in increased skin lesions of infested birds.

Behavioral resistance of M. domestica to imidacloprid and methomyl persists. Spinosad and cyantraniliprole baits (delayed mortality) performed best. Speed of action may be a factor in use and misuse of baits.