Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Color and 3-D Information
Inkyu Sa, Christopher F. Lehnert, Andrew English, Chris McCool, Feras Dayoub, Ben Upcroft, Tristan Perez
Abstract
This letter presents a three-dimensional (3-D) 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 3-D 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 letter makes use of both color and geometry information acquired from an RGB-D sensor and utilizes a supervised-learning approach for the peduncle detection task. The performance of the proposed method is demonstrated and evaluated by 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 3-D sweet pepper and peduncle images to assist the research community in performing further research on this topic.
BibTeX
@inproceedings{ral2017_peduncledetectio,
title = {Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Color and 3-D Information},
author = {Inkyu Sa and Christopher F. Lehnert and Andrew English and Chris McCool and Feras Dayoub and Ben Upcroft and Tristan Perez},
booktitle = {RA-L 2017},
year = {2017}
}