Explainable AI for clove quality grading: Benchmarking post hoc XAI and compositional interpretability under domain shift

Reliable agricultural commodity grading requires accuracy and explanations that match regulatory criteria. Using 4603 expert-graded clove images across four Zanzibar State Trading Corporation (ZSTC) quality grades, we benchmark seven post hoc explainable artificial intelligence (XAI) methods (Grad-CAM, Grad-CAM++, ScoreCAM, LIME, GradientSHAP, CLS-Attention, and Chefer LRP) across eight CNN/ViT architectures and compare them with a compositional segmentation–classification pipeline that builds interpretability into the model. We propose the Explanation Energy Ratio (EER) to quantify foreground-aligned explanations using ground-truth masks. EER varies widely by architecture explainer, with different explainers yielding different results for the same classifier. Under a synthetic background-replacement domain shift, robustness differs despite similar in-domain performance. LIME performs well on transformers but is slow; SHAP is too costly for edge use. The compositional pipeline achieves 99.45% F1 with 100% EER by design and is preferred by certified ZSTC graders for regulatory auditing.