Fine-Grain Prediction of Strawberry Freshness using Subsurface Scattering
Fine-Grain Prediction of Strawberry Freshness using Subsurface Scattering
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DOI：
10.1109/iccvw54120.2021.00264
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Jeremy Klotz;Vijay Rengarajan;Aswin C. Sankaranarayanan
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作者：
Jeremy Klotz;Vijay Rengarajan;Aswin C. Sankaranarayanan
在任何可见的腐烂之前预测水果的新鲜度对于跨越生产商、零售商和消费者的食品分销链来说是非常宝贵的。在这项工作中，我们利用与草莓组织相关的次表面散射签名来进行长期的可食用性预测。具体来说，我们实施各种主动照明技术与投影仪相机系统来测量草莓的次表面散射和预测的时间时，它可能是不可食用的。我们提出了一种基于学习的方法，在结构化照明下捕获来执行此预测。我们通过捕获草莓随时间自然腐烂的数据集来研究我们方法的有效性。
Predicting fruit freshness before any visible decay is invaluable in the food distribution chain, spanning producers, retailers, and consumers. In this work, we leverage subsurface scattering signatures associated with strawberry tissue to perform long-term edibility predictions. Specifically, we implement various active illumination techniques with a projector-camera system to measure a strawberry’s sub-surface scattering and predict the time when it is likely to be inedible. We propose a learning-based approach with captures under structured illumination to perform this prediction. We study the efficacy of our method by capturing a dataset of strawberries decaying naturally over time.