发表机构
University of the Western Cape（西开普大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对跃变型水果剩余货架期回归，本文提出样本级交叉验证优于观察级划分，并利用Grad-CAM分析轻量级视觉模型的归因差异，为纵向预测评估提供可靠协议。
AI 中文摘要
从图像估计剩余货架期（RSL）可为易腐农产品提供经济实惠的决策支持，但当同一生物样本存在重复图像时，评估协议会显著影响报告的性能。我们使用Hass鳄梨成熟度数据集（包含来自426个果实、三种储存条件下的8,834个图像-RSL配对）来评估一个冻结的ImageNet预训练视觉骨干网络搭配轻量级回归头。我们的贡献有三方面：量化了观察级与样本分离评估的影响，在样本分离交叉验证下比较了轻量级和较重的视觉骨干网络，并使用Grad-CAM检查了它们的空间归因。在十次观察级随机划分中，模型实现了平均均方根误差（RMSE）为2.37天，标准差为0.03天；而样本分离的5折交叉验证产生的平均RMSE为3.12天，标准差为0.11天。相应的平均决定系数为0.553。一项匹配的逐样本比较确认了样本分离评估下的更高误差，在426个样本上的概率值低于0.001，表明对于该数据集和模型配置，观察级划分给出了显著更乐观的估计。在样本分离评估下，MobileNetV3-Small（93万参数）达到了与ResNet-18相当的准确性，同时提供了显著更高的吞吐量，且Grad-CAM揭示了轻量级骨干网络之间空间归因的差异。这些结果支持在评估用于纵向货架期预测的轻量级视觉模型时采用样本分离评估和归因分析。
英文摘要
Estimating remaining shelf life (RSL) from images could provide affordable decision support for perishable produce, but evaluation protocols can substantially affect reported performance when repeated images are available from the same biological specimen. We use the Hass Avocado Ripening dataset, comprising 8,834 image-RSL pairs from 426 fruits across three storage regimes, to evaluate a frozen ImageNet-pretrained visual backbone with a lightweight regression head. Our contributions are threefold: we quantify the effect of observation-level versus specimen-disjoint evaluation, compare lightweight and heavier visual backbones under specimen-disjoint cross-validation, and examine their spatial attributions using Grad-CAM. Across ten observation-level random splits, the model achieves a mean RMSE of 2.37 days with a standard deviation of 0.03 days, whereas specimen-disjoint 5-fold cross-validation yields a mean RMSE of 3.12 days with a standard deviation of 0.11 days. The corresponding mean coefficient of determination is 0.553. A matched per-specimen comparison confirms higher error under specimen-disjoint evaluation, with a probability value below 0.001 across 426 specimens, showing that observation-level partitioning gives a substantially more optimistic estimate for this dataset and model configuration. Under specimen-disjoint evaluation, MobileNetV3-Small (0.93 million parameters) achieves accuracy comparable to ResNet-18 while providing substantially higher throughput, and Grad-CAM reveals differences in spatial attribution between the lightweight backbones. These results support specimen-disjoint evaluation and attribution analysis when assessing lightweight vision models for longitudinal shelf-life prediction.
Comments7 pages, 1 figure, 4 tables
