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

## TL;DR

This paper introduces a combined colour and 3D information method using supervised learning for accurate peduncle detection of sweet peppers in the field, crucial for autonomous harvesting.

## Contribution

It presents a novel 3D visual detection approach utilizing RGB-D data and supervised learning, achieving an AUC of 0.71 for peduncle detection in real field conditions.

## Key findings

- Achieved an AUC of 0.71 in peduncle detection
- Utilized combined colour and 3D geometric data
- Provided annotated datasets for future research

## Abstract

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.