Rapid and Interpretable Wheat Seed Variety Identification Using Morphology-Guided Feature Engineering and Ensemble Learning

Confirming seed variety identity is important in certification, breeding-material management and grain trade, but visually similar cultivars remain difficult to distinguish consistently. We present GAFE-Stack, a morphology-guided classifier that expands seven kernel measurements into twenty-one interpretable descriptors and combines five complementary learners. The method is evaluated on the small, balanced public UCI Seeds benchmark ($blackN=210$; $black70$ kernels per variety), whose measurements were extracted from soft X-ray images. Across ten repeats of stratified five-fold cross-validation, GAFE-Stack achieved $96.33black\pm 2.29\%$ accuracy and $96.67\%$ under leave-one-out validation. Its observed mean differences from seven re-implemented references ranged from $black0.48$ to $black4.29$ percentage points; after accounting for dependence among repeated folds and applying Holm adjustment, none of the comparisons was significant at $black\alpha =0.05$. The strongest individual member, LightGBM, achieved a slightly higher mean accuracy ($black96.76\%$), whereas GAFE-Stack had lower fold-level dispersion ($black2.29\%$ versus $black2.60\%$) and fewer pooled Kama–Canadian confusions than the RBF-SVM baseline. The complete pipeline achieved $black93.33\%$ and $black94.52\%$ accuracy with $black30$ and $black60$ labelled training kernels, respectively, compared with $black96.33\%$ using the full training folds. From stored morphometric inputs, single-thread CPU training required $black0.85$ s and batch prediction processed approximately $black52,600$ kernels/s without a GPU. Applying the same dimension-typed construction rules to Raisin, Rice and Dry Bean datasets produced small positive mean changes of $black0.09$–$black0.37$ points, with corrected intervals including zero. Lot-purity results are reported only as an exploratory resampling analysis of UCI observations. The present evidence therefore supports GAFE-Stack as an interpretable proof-of-concept approach to seed screening under benchmark conditions; validation on independently acquired kernels, measurement systems and physical seed lots remains future work.