Validation evidence for green chromatic coordinate (GCC) calculations produced by GRIME AI (GaugeCam Remote Image Manager Educational Artificial Intelligence). Synthetic test images with known color properties were generated using a Python script producing two image types used in the GCC comparison: solid-color images with uniform fill (flat) and solid-color images with a stipple effect applied via Gaussian-blurred random noise (stippled). Both types span 65 green hues across an 8 x 8 grid of HSV saturation and value levels plus one pure green [0, 255, 0] image, at a resolution of 1024 x 768 pixels saved as JPEG. The script additionally produces splotch images — circular green patches composited onto a clay-colored background in flat and stippled permutations — which are included in the deposit but were not used in the GCC comparison analyses. GCC values were independently computed by GRIME AI and by the PhenoCam Network pipeline (Northern Arizona University) and manually compiled into a spreadsheet for comparison.

GCC values computed by GRIME AI were compared against GCC values computed by the PhenoCam Network pipeline, used here as a benchmark reference, across both flat and stippled solid image sets. Agreement between the two implementations was near-perfect: flat images yielded a mean absolute difference of 0.0% (SD = 0.00003) and stippled images yielded a mean absolute difference of 0.0% (SD = 0.0002). These statistically insignificant discrepancies are attributed to minor floating-point rounding differences introduced by CPU architecture variation during image processing rather than any methodological inconsistency, confirming that GRIME AI produces reproducible and accurate greenness calculations consistent with an established community standard.

A secondary analysis examined the effect of image serialization on GCC consistency by comparing GRIME AI calculations performed on images held in memory against calculations performed on those same images after saving to disk and reloading. Results were evaluated separately for flat images (mean difference = 0.02%, SD = 0.00383) and stippled images (mean difference = 0.03%, SD = 0.00344). Both differences are traced to JPEG lossy compression, which introduces small per-channel rounding errors at the pixel level (e.g., a pure green pixel [0, 255, 0] becomes [0, 255, 1] after a save-reload cycle). The slightly larger variability in stippled images is consistent with greater spatial heterogeneity providing more opportunity for compression artifacts to accumulate. The dataset includes the image generation script, all synthetic test images, and a spreadsheet recording GCC outputs from both GRIME AI and PhenoCam alongside computed differences.