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Cell phone image analysis as a proxy for laboratory egg quality measurements
Anna Wolc1, Usamah Kabuye2, Tricia Veldhuizen2
1Department of Animal Science, Iowa State University, Ames, IA, USA; Hy-Line International, Dallas Center, IA, USA.
Poultry Science
|August 3, 2026
Summary
Smartphone image analysis accurately measures egg quality traits like color and weight, replacing lab equipment. This technology enhances genetic improvement programs by enabling field data collection, even in resource-limited settings.
Area of Science:
- Agricultural Science
- Animal Breeding
- Image Analysis
Background:
- Accurate egg quality assessment is crucial for genetic improvement in poultry breeding programs.
- Traditional laboratory instruments for measuring egg traits are often inaccessible in field settings or developing countries.
- There is a need for portable, cost-effective methods to assess egg quality at the individual level.
Purpose of the Study:
- To evaluate the accuracy of smartphone camera-derived measurements for egg quality traits.
- To validate these measurements against standard laboratory references for shell color, egg weight, and defect classification.
- To assess the potential of smartphone image analysis for genetic improvement programs in resource-limited environments.
Main Methods:
- 359 matched eggs (brown and white, normal and defective) were photographed using a smartphone on a tripod.
- A custom Python image pipeline, utilizing SAM3 segmentation, extracted color (CIE L*a*b*, chroma), morphometric (area, axes, shape index), and defect features.
- Image-derived data were validated against laboratory measurements (Minolta colorimeter, weighing scale) using statistical analysis and machine learning models (Random Forest, Gradient Boosting, SVM).
Main Results:
- Smartphone-derived color components showed high correlation with colorimeter values (Pearson r ≥ 0.986).
- Egg weight was accurately predicted from morphometric measurements (cross-validated R² = 0.922 overall).
- A soft-voting ensemble model achieved good performance in binary defect classification (AUC = 0.810 ± 0.078), outperforming simpler models.
Conclusions:
- Smartphone-based image analysis provides accurate and reliable measurements for egg color and weight, comparable to laboratory methods.
- This technology offers a viable alternative to traditional instruments, overcoming accessibility limitations in field and developing country settings.
- The findings support the integration of smartphone image analysis into poultry breeding programs for enhanced genetic improvement.
