Reflected light spectrometry and ai-based data analysis for detection of rapid chicken eggshell change caused by mycoplasma synoviae

Abstract

Mycoplasma synoviae (MS) is a pathogen that causes economic losses in the poultry industry. It can be transmitted, amongst others, via the respiratory tract and spread relatively quickly. As such, MS infections are mainly controlled by maintaining MS-free breeder flocks. Routine diagnosis for the detection of MS may be based on serological, culture, and molecular tests. Here, we propose an optical solution where AI-based analysis of spectral data obtained from the light reflected from the eggshells is used to determine whether they originate from healthy or Mycoplasma synoviae-infected hens. The wavelengths proposed for spectral MS detection are limited to those of VIS and NIR DPSS lasers, which are freely accessible on market. The results are satisfactory: for white eggshells, the F-score is over 95% for five different combinations of wavelengths (using eight or nine wavelengths); for brown eggshells, the F-score is above 85%, also for five different combinations of 6–9 wavelengths.