# Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models

arXiv: https://arxiv.org/abs/2412.09739 · HTML: https://mlsanity.com/paper/2412.09739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [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)

## Built on by (most cited first, 1 of 1)

- [Modeling Time-Lapse Trajectories to Characterize Cranberry Growth](https://mlsanity.com/paper/2510.08901/) (2025)

## Abstract (arXiv)

Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation. Using aerial and ground imaging over a time series, we develop a framework for characterizing the ripening process of cranberry crops, a crucial component for precision agriculture tasks such as comparing crop breeds (high-throughput phenotyping) and detecting disease. Using drone imaging, we capture images from 20 waypoints across multiple bogs, and using ground-based imaging (hand-held camera), we image same bog patch using fixed fiducial markers. Both imaging methods are repeated to gather a multi-week time series spanning the entire growing season. Aerial imaging provides multiple samples to compute a distribution of albedo values. Ground imaging enables tracking of individual berries for a detailed view of berry appearance changes. Using vision transformers (ViT) for feature detection after segmentation, we extract a high dimensional feature descriptor of berry appearance. Interpretability of appearance is critical for plant biologists and cranberry growers to support crop breeding decisions (e.g. comparison of berry varieties from breeding programs). For interpretability, we create a 2D manifold of cranberry appearance by using a UMAP dimensionality reduction on ViT features. This projection enables quantification of ripening paths and a useful metric of ripening rate. We demonstrate the comparison of four cranberry varieties based on our ripening assessments. This work is the first of its kind and has future impact for cranberries and for other crops including wine grapes, olives, blueberries, and maize. Aerial and ground datasets are made publicly available.