AI Classification of Eggs’ Origin from Mycoplasma synoviae-Infected or Non-Infected Poultry via Analysis of the Spectral Response

Abstract

Rapid detection of Mycoplasma synoviae (MS) in a flock is crucial from the perspective of animals’ health and economic income. MS are highly contagious bacteria that can cause significant losses in commercial poultry populations when its prevalence is not limited. MS infections can cause losses associated with a range of clinical symptoms related to the respiratory, mobility and reproductive systems. Lesions related to the reproductive system and changes in the eggshell result in losses associated with infection or embryo death or complete destruction of the eggs. The authors propose using spectral measurements backed up by an AI data processing algorithm to classify eggs’ origin: from healthy hens or MS-infected ones. The newest obtained classification factors are F-scores for white eggshells of 99% and scores for brown eggshells of 99%—all data used for classification were obtained using a portable multispectral fibre-optics reflectometer. The proposed method may be used directly on the farm by staff members with limited qualifications, as well as by veterinary doctors, assistants, or customs officers. Moreover, this method is scalable to mass production of eggs. Standard methods such as serological tests require either specialized staff or laboratory equipment. Implementation of a non-destructive optical measurement method, which is easily adapted for use on a production line, is economically reasonable.