APPLENMR-TEXT: A COST-SENSITIVE LF-NMRBENCHMARK FOR APPLE MOLDY-CORE SCREENING

Apple moldy-core screening is a small-data, cost-sensitive task: the defect is in-ternal, destructive verifcation is needed for labels, and expert diagnostic notes areunavailable as inputs for new fruit. We introduce AppleNMR-Text, a benchmarkof 237 Red Fuji apples with LF-NMR slices, destructively verifed labels, andexpert-reviewed text, together with fxed cross-validation and quality-gated utilityevaluation. As a documented image-input benchmark model, we study a two-stage pipeline: dual-memory retrieval guides structured rationale generation, thena classifer combines the image, generated rationale, and sanitized evidence. Onthe reported fve-fold evaluation, this model obtains TAAPM 393.733± 13.973,versus 158.40± 224.01 for vision-only VGG19 and 155.33± 61.98 for directlyfne-tuned Qwen3-VL-8B. Dual-memory retrieval outperforms either single memory on TAAPM. Balanced accuracy is 0.700± 0.019, so the utility advantageshould not be read as uniform superiority on conventional metrics. AppleNMR-Text provides a controlled testbed for image-only screening and for separating anexpert-text oracle from a deployable input protocol; its single-season cohort doesnot establish cross-domain generalization.