Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 2, Issue 2, Pages 765-772Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2017.2651952
Keywords
Agricultural automation; computer vision for automation; RGB-D perception; robotics in agriculture and forestry
Categories
Funding
- Queensland Department of Agriculture and Fisheries
- Queensland University of Technology Strategic Investment in Farm Robotics Program
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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 precisionrecall 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.
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