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

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.

8 pages, 14 figures, Robotics and Automation Letters

Cited by in corpus (4)

• Autonomous Sweet Pepper Harvesting for Protected Cropping Systems
• Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends
• In-Field Peduncle Detection of Sweet Peppers for Robotic Harvesting: a comparative study
• deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques