RiMS-FiLM: Non-Destructive Multimodal Screening for Fixed-Budget Re-Inspection Prioritization of Maize Kernels

Rapid, non-destructive prioritization of maize kernels can help allocate limited re-inspection capacity in post-harvest quality control. Existing deep learning approaches mainly optimize overall classification accuracy and do not explicitly address which samples should be inspected first when verification resources are constrained. This study proposes RiMS-FiLM, an end-to-end multimodal screening framework that integrates kernel RGB appearance with lightweight physical attributes (weight and size) to generate re-inspection priority scores. Feature-wise Linear Modulation adaptively conditions visual representations on physical measurements, while a prototype-guided module organizes fused embeddings into class-structured operational quality-priority regions. The risk tiers are derived from the original GrainSet-Maize defect categories and are used as screening-priority labels rather than laboratory-validated toxicological endpoints. Internal evaluation on the stratified split of the single public GrainSet-Maize dataset over five random seeds yielded a Macro-F1 of 0.9917 ± 0.0027 and captured 94.65 ± 0.21% of high-priority samples within a 20% re-inspection budget. It also reduced severe high-to-low priority errors compared with representative fusion and selective-screening baselines. These results demonstrate the potential of low-cost multimodal sensing for resource-constrained, non-destructive grain quality screening and re-inspection planning.