# Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors

arXiv: https://arxiv.org/abs/2004.08501 · HTML: https://mlsanity.com/paper/2004.08501/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 16 of 16)

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- [Detecting Olives with Synthetic or Real Data? Olive the Above](https://mlsanity.com/paper/2308.08271/) (2023)
- [Wild Berry image dataset collected in Finnish forests and peatlands using drones](https://mlsanity.com/paper/2405.07550/) (2024)
- [Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models](https://mlsanity.com/paper/2412.09739/) (2024)
- [Modeling Time-Lapse Trajectories to Characterize Cranberry Growth](https://mlsanity.com/paper/2510.08901/) (2025)
- [Learning with Weak Annotations for Robust Maritime Obstacle Detection](https://mlsanity.com/paper/2206.13263/) (2022)
- [Vision-Based Cranberry Crop Ripening Assessment](https://mlsanity.com/paper/2309.00028/) (2023)
- [Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones](https://mlsanity.com/paper/2501.02344/) (2025)
- [The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning](https://mlsanity.com/paper/2505.08537/) (2025)

## Abstract (arXiv)

Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers. In this work, we present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. Notably, supervision is done using low cost center point annotations. The approach, named Triple-S Network, incorporates a three-part loss with shape priors to promote better fitting to objects of known shape typical in agricultural scenes. Our results improve overall segmentation performance by more than 6.74% and counting results by 22.91% when compared to state-of-the-art. To train and evaluate the network, we have collected the CRanberry Aerial Imagery Dataset (CRAID), the largest dataset of aerial drone imagery from cranberry fields. This dataset will be made publicly available.