Cell phone image analysis as a proxy for laboratory egg quality measurements.

Precise individual-level recording of egg quality for genetic improvement requires laboratory instruments often unavailable at field test locations or in developing countries where breeding programs where local breeds are being developed. This study evaluated smartphone camera-derived measurements validating them against laboratory references for shell color (measured with Minolta colorimeter), egg weight, and defect classification in 359 matched eggs (180 brown, 179 white; 180 defective, 179 normal). Eggs were segmented out using SAM3 text-prompted segmentation applied to each raw photograph. A custom Python image pipeline extracted CIE L* (lightness), a*(redness on red-green scale), b*(yellowness on blue-yellow scale), chroma, morphometric axes, projected area, shape index, and surface defect scores from a mean of 2 images per egg acquired with a smartphone stabilized on a tripod, additionally a scale calibration from a physical reference sticker was considered. Combined-group validation statistics for image-derived color components against colorimeter values were high (Pearson r ≥ 0.986, 10-fold cross-validated R