Fine-Grain Prediction of Strawberry Freshness Using Subsurface Scattering
Abstract
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.
Cite
Text
Klotz et al. "Fine-Grain Prediction of Strawberry Freshness Using Subsurface Scattering." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00264Markdown
[Klotz et al. "Fine-Grain Prediction of Strawberry Freshness Using Subsurface Scattering." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/klotz2021iccvw-finegrain/) doi:10.1109/ICCVW54120.2021.00264BibTeX
@inproceedings{klotz2021iccvw-finegrain,
title = {{Fine-Grain Prediction of Strawberry Freshness Using Subsurface Scattering}},
author = {Klotz, Jeremy and Rengarajan, Vijay and Sankaranarayanan, Aswin C.},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2021},
pages = {2328-2336},
doi = {10.1109/ICCVW54120.2021.00264},
url = {https://mlanthology.org/iccvw/2021/klotz2021iccvw-finegrain/}
}