Visual food recognition systems deployed in real-world environments, such as automated conveyor-belt inspection, are highly sensitive to domain shifts caused by illumination changes. While recent studies have shown that lighting variations can significantly distort food perception by both humans and AI, existing works are often limited to single food categories or controlled settings, and most public food datasets lack explicit illumination annotations. In this work, we investigate illumination-induced domain shift in multi-class food category recognition using two widely adopted datasets, Food-101 and Fruits-360. We demonstrate substantial accuracy degradation under cross-dataset evaluation due to mismatched visual conditions. To address this challenge, we construct synthetic illumination-augmented datasets by systematically varying light temperature and intensity, enabling controlled robustness analysis without additional labels. We further evaluate cross-dataset transfer learning and domain generalization, with a focus on illumination-sensitive target categories such as apple-based classes. Experimental results show that illumination-aware augmentation significantly improves recognition robustness under domain shift while preserving real-time performance. Our findings highlight the importance of illumination robustness and provide practical insights for deploying reliable food recognition systems in real-world inspection scenarios.

翻译：在现实环境中部署的视觉食品识别系统（如自动化传送带检测）对光照变化引起的域偏移高度敏感。尽管近期研究表明光照变化会显著扭曲人类和人工智能对食品的感知，但现有研究通常局限于单一食品类别或受控环境，且大多数公开食品数据集缺乏明确的光照标注。本研究利用两个广泛采用的数据集Food-101和Fruits-360，探究了多类别食品识别中光照诱导的域偏移问题。我们证明了在跨数据集评估中，由于视觉条件不匹配会导致显著的准确率下降。为应对这一挑战，我们通过系统改变色温和光照强度构建了合成光照增强数据集，从而在无需额外标注的情况下实现可控的鲁棒性分析。我们进一步评估了跨数据集迁移学习和域泛化方法，重点关注苹果类等对光照敏感的目标类别。实验结果表明，光照感知增强能显著提升域偏移下的识别鲁棒性，同时保持实时性能。我们的研究结果凸显了光照鲁棒性的重要性，并为在现实检测场景中部署可靠的食品识别系统提供了实用见解。