# Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2004.14010 · HTML: https://mlsanity.com/paper/2004.14010/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 11 of 11)

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- [Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks](https://mlsanity.com/paper/2105.10325/) (2021)
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- [Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod](https://mlsanity.com/paper/2204.10902/) (2022)
- [Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties](https://mlsanity.com/paper/2403.12935/) (2024)
- [The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning](https://mlsanity.com/paper/2505.08537/) (2025)
- [ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation](https://mlsanity.com/paper/2605.24353/) (2026)

## Abstract (arXiv)

The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH.