A cost-effective imaging system for monitoring poultry behaviour in small-scale kenyan poultry sheds
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Kuupäev
2023
Kättesaadav alates
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Estonian University of Life Sciences
Abstrakt
The objective of this paper was to develop a low-cost prototype poultry behaviour
imaging and analysis system for monitoring intensively-reared flocks suitable for small-scale
Kenyan poultry sheds. An image processing and analysis programme was developed using
Python programming language and the OpenCV image processing package. This was tested on
overhead images of Ross 308 birds collected over a number of days using a Raspberry Pi V2 camera.
A second experiment using toy-chicks was conducted with an angled camera (Wansview W3).
Linear transformation (LT) and background subtraction (BS) methods were applied and
compared for effectiveness at detecting yellow and brown toy-chicks on woodchip bedding.
Perspective transformation (PT) was applied and evaluated for its ability to transform the angled
images into two-dimensional views. In the first experiment, where white birds were detected
against a dark background, LT object detection successfully detected 99.8% of birds in the
sampled images. However, in the second experiment, the LT method was just 56.5% effective at
detecting the yellow toy-chicks against the light-coloured background. In contrast, the BS method
was more effective, detecting 91.5% of the yellow toy-chicks. The results showed that BS
detection success was worse for yellow toy-chicks in the far section, detecting 83% as opposed
to 100% of those in the near-section. Edge processing of the image processing algorithm was
tested on a Raspberry Pi 3 series B+ computer. This prototype provides a solid foundation for
further development and testing of low-cost, automated poultry monitoring systems capable of
reporting on thermal comfort inferred from cluster index.
Kirjeldus
Received: February 1st, 2023 ; Accepted: April 29th, 2023 ; Published: May 3rd, 2023 ; Correspondence: speets@harper-adams.ac.uk
Märksõnad
background subtraction, cluster index, image processing, linear transformation, poultry, articles