Pakuła, A.; Paśko, S.; Marć, P.; Kursa, O.; Jaroszewicz, L.R. AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response. Appl. Sci.2023, 13, 12360.
Pakuła, A.; Paśko, S.; Marć, P.; Kursa, O.; Jaroszewicz, L.R. AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response. Appl. Sci. 2023, 13, 12360.
Pakuła, A.; Paśko, S.; Marć, P.; Kursa, O.; Jaroszewicz, L.R. AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response. Appl. Sci.2023, 13, 12360.
Pakuła, A.; Paśko, S.; Marć, P.; Kursa, O.; Jaroszewicz, L.R. AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response. Appl. Sci. 2023, 13, 12360.
Abstract
Mycoplasma synoviae (MS) is a highly contagious bacteria that can cause significant harm in commercial poultry populations while not prevented. Rapid detection of its presence in a flock is crucial from the perspective of animals' health and economic income. Authors propose spectral measurements strongly backed up by the AI data processing algorithms for classifying egg origin: from healthy hens or MS-infected ones. The newest obtained classification factors are F-scores for white eggshells 99% and for brown eggshells 99%—all data used for classification were taken by the portable multispectral fibre-optics reflectometer.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
