# Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information

arXiv: https://arxiv.org/abs/1701.08608 · HTML: https://mlsanity.com/paper/1701.08608/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2017.

## Built on by (most cited first, 6 of 6)

- [Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots](https://mlsanity.com/paper/2303.08923/) (2023)
- [deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques](https://mlsanity.com/paper/2203.09091/) (2022)
- [Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach](https://mlsanity.com/paper/2505.03702/) (2025)
- [MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters](https://mlsanity.com/paper/2310.05762/) (2023)
- [Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting](https://mlsanity.com/paper/2105.03832/) (2021)
- [3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud](https://mlsanity.com/paper/2311.04699/) (2023)

## Abstract (arXiv)

This paper presents a 3D visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field. Cutting the peduncle cleanly is one of the most difficult stages of the harvesting process, where the peduncle is the part of the crop that attaches it to the main stem of the plant. Accurate peduncle detection in 3D space is therefore a vital step in reliable autonomous harvesting of sweet peppers, as this can lead to precise cutting while avoiding damage to the surrounding plant. This paper makes use of both colour and geometry information acquired from an RGB-D sensor and utilises a supervised-learning approach for the peduncle detection task. The performance of the proposed method is demonstrated and evaluated using qualitative and quantitative results (the Area-Under-the-Curve (AUC) of the detection precision-recall curve). We are able to achieve an AUC of 0.71 for peduncle detection on field-grown sweet peppers. We release a set of manually annotated 3D sweet pepper and peduncle images to assist the research community in performing further research on this topic.