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Fluorescence imaging and deep learning for detection of low-contrast surface contamination on eggshell

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2026 ASABE Annual International Meeting  2601444.(doi:10.13031/aim.202601444)
Authors:   Alin Khaliduzzaman, Tanea Gray, Mamunur Rahman, Naoshi Kondo, Isabella C.F.S. Condotta
Keywords:   Eggshell, surface quality, computer vision, object detection, egg industry 4.0.

Abstract. Ensuring eggshell surface cleanliness is important for product quality and food safety. Routine inspection under visible illumination can identify gross residues but may miss thin or low-contrast surface films that are difficult to perceive consistently, particularly at industrial speeds. Eggshell surfaces can also carry microorganisms, motivating improved, non-destructive sensing methods. In this study, we evaluated fluorescence imaging as a contrast-enhancement modality for visualizing fecal-smear contamination not readily apparent under standard illumination, and we developed a computer vision workflow for automated detection. Fluorescence images were annotated in CVAT and used to train Ultralytics YOLO11s and YOLO26s object detection models implemented in PyTorch. Images were partitioned into training (70%) and validation (30%) sets. The models‘ performance was assessed on a validation set using precision, recall, and mean average precision (mAP50 and mAP50-95). On the validation set, the YOLO11 model achieved little higher accuracy (mAP50 = 0.995 and mAP50-95 = 0.995) but inference time was higher compared to YOLO26 for distinguishing clean shells from fecal-smear contamination under the study‘s imaging conditions. Inference time was 194.6ms for YOLO11 whereas 165.3ms for YOLO26 per image on a CPU (Intel i7-12700). These results indicate that fluorescence-assisted imaging combined with deep-learning can support rapid, non-destructive screening for eggshell surface residues and merits further validation using larger datasets that vary by contamination presentation, imaging conditions, and independent production lots.

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