# Vision-Based Cranberry Crop Ripening Assessment

arXiv: https://arxiv.org/abs/2309.00028 · HTML: https://mlsanity.com/paper/2309.00028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)
- [Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework](https://mlsanity.com/paper/2107.12746/) (2021)
- [One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation](https://mlsanity.com/paper/2104.02246/) (2021)
- [Pointly-Supervised Instance Segmentation](https://mlsanity.com/paper/2104.06404/) (2021)
- [Weakly Supervised Instance Segmentation using Class Peak Response](https://mlsanity.com/paper/1804.00880/) (2018)
- [Incremental Learning in Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2112.01882/) (2021)
- [Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors](https://mlsanity.com/paper/2004.08501/) (2020)
- [AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk](https://mlsanity.com/paper/2011.04064/) (2020)

## Abstract (arXiv)

Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation. Using drone imaging, we develop a framework for characterizing the ripening process of cranberry crops. Our method consists of drone-based time-series collection over a cranberry growing season, photometric calibration for albedo recovery from pixels, and berry segmentation with semi-supervised deep learning networks using point-click annotations. By extracting time-series berry albedo measurements, we evaluate four different varieties of cranberries and provide a quantification of their ripening rates. Such quantification has practical implications for 1) assessing real-time overheating risks for cranberry bogs; 2) large scale comparisons of progeny in crop breeding; 3) detecting disease by looking for ripening pattern outliers. This work is the first of its kind in quantitative evaluation of ripening using computer vision methods and has impact beyond cranberry crops including wine grapes, olives, blueberries, and maize.