# Weakly and Semi-Supervised Detection, Segmentation and Tracking of Table Grapes with Limited and Noisy Data

arXiv: https://arxiv.org/abs/2208.13001 · HTML: https://mlsanity.com/paper/2208.13001/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 27 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://mlsanity.com/paper/1708.07120/) (2017)
- [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](https://mlsanity.com/paper/1907.11819/) (2019)
- [Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards](https://mlsanity.com/paper/1610.08120/) (2016)
- [Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution](https://mlsanity.com/paper/1911.03098/) (2019)
- [Counting of Grapevine Berries in Images via Semantic Segmentation using Convolutional Neural Networks](https://mlsanity.com/paper/2004.14010/) (2020)
- [Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1905.00458/) (2019)

## Built on by (most cited first, 7 of 7)

- [Open-World Semantic Segmentation Including Class Similarity](https://mlsanity.com/paper/2403.07532/) (2024)
- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for robotics in precision agriculture](https://mlsanity.com/paper/2309.13393/) (2023)
- [Open-World Panoptic Segmentation](https://mlsanity.com/paper/2412.12740/) (2024)
- [A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management](https://mlsanity.com/paper/2509.20580/) (2025)
- [Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery](https://mlsanity.com/paper/2502.18320/) (2025)
- [Synthetic Data Generation for Anomaly Detection on Table Grapes](https://mlsanity.com/paper/2412.12949/) (2024)

## Abstract (arXiv)

Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks for precision agriculture, enabling robotic harvesting and yield estimation applications. However, modern algorithms are data hungry and it is not always possible to gather enough data to apply the best performing supervised approaches. Since data collection is an expensive and cumbersome task, the enabling technologies for using computer vision in agriculture are often out of reach for small businesses. Following previous work in this context, where we proposed an initial weakly supervised solution to reduce the data needed to get state-of-the-art detection and segmentation in precision agriculture applications, here we improve that system and explore the problem of tracking fruits in orchards. We present the case of vineyards of table grapes in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the case in which there is some initial labelled data that could work as source data (eg wine grape data), but it is considerably different from the target data (e.g. table grape data). To improve detection and segmentation on the target data, we propose to train the segmentation algorithm with a weak bounding box label, while for tracking we leverage 3D Structure from Motion algorithms to generate new labels from already labelled samples. Finally, the two systems are combined in a full semi-supervised approach. Comparisons with state-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images and with very simple labelling.