On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture

Sa, Inkyu, McCool, Christopher, Lehnert, Christopher, & Perez, Tristan (2015) On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture. In ICRA 2015 : IEEE International Conference on Robotics and Automation, 26 -30th May 2015, Seattle, Washington.


View at publisher


Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

47 since deposited on 16 Nov 2015
47 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 90394
Item Type: Conference Item (Poster)
Refereed: Yes
Additional URLs:
Divisions: Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 The Authors
Deposited On: 16 Nov 2015 04:41
Last Modified: 17 Nov 2015 06:29

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page