期刊
BIOSYSTEMS ENGINEERING
卷 186, 期 -, 页码 34-44出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2019.06.019
关键词
RGB-D image; Bayes classifier; Density clustering; Support vector machine; Citrus-harvesting robot
资金
- National Natural Science Foundation of China [31571568]
In-field citrus detection and localisation are highly challenging tasks due to varying illumination conditions, partial occlusion of citrus, and the colour variation of citrus at different stages of maturity. A reliable algorithm based on red-green-blue-depth (RGB-D) images was developed to detect and locate citrus in real, outdoor orchard environments for robotic harvesting. A depth filter and a Bayes-classifier-based image segmentation method were first developed to exclude as many backgrounds as possible. A density clustering method was then used to group adjacent points in the filtered RGB-D images into clusters, where each cluster represents a possible citrus. A colour, gradient, and geometry feature-based support vector machine classifier was trained to remove false positives. To test the method, a dataset with 506 RGB-D images was acquired in a citrus orchard on sunny and cloudy days. Results showed that the proposed algorithm was robust with an F1 score of 0.9197; the positioning errors in the x, y and z directions were 7.0 +/- 2.5 mm, -4.0 +/- 3.0 mm and 13.0 +/- 3.0 mm, respectively, and the sizing error was -1.0 +/- 4.0 mm. These excellent performance values demonstrate that the proposed method could be used to guide a citrus-harvesting robot. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.
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