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Sensors 2015, 15(8), 20463-20479; doi:10.3390/s150820463

In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation

1
The Research Center for Coastal Environmental Engineering and Technology of Shandong Province, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2
School of Electrical Engineering, Pusan National University, Busan 609-735, Korea
3
Division of Plant Environment, Gyeongsangnam-Do Agricultural Research and Extension Services, Jinju 660-985, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 1 June 2015 / Revised: 15 July 2015 / Accepted: 23 July 2015 / Published: 19 August 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3206 KB, uploaded 19 August 2015]   |  

Abstract

In this paper, we present a challenging task of 3D segmentation of individual plant leaves from occlusions in the complicated natural scene. Depth data of plant leaves is introduced to improve the robustness of plant leaf segmentation. The low cost RGB-D camera is utilized to capture depth and color image in fields. Mean shift clustering is applied to segment plant leaves in depth image. Plant leaves are extracted from the natural background by examining vegetation of the candidate segments produced by mean shift. Subsequently, individual leaves are segmented from occlusions by active contour models. Automatic initialization of the active contour models is implemented by calculating the center of divergence from the gradient vector field of depth image. The proposed segmentation scheme is tested through experiments under greenhouse conditions. The overall segmentation rate is 87.97% while segmentation rates for single and occluded leaves are 92.10% and 86.67%, respectively. Approximately half of the experimental results show segmentation rates of individual leaves higher than 90%. Nevertheless, the proposed method is able to segment individual leaves from heavy occlusions. View Full-Text
Keywords: plant monitoring; occlusions; leaf detection; mean shift; center of divergence; automatic initialization plant monitoring; occlusions; leaf detection; mean shift; center of divergence; automatic initialization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xia, C.; Wang, L.; Chung, B.-K.; Lee, J.-M. In Situ 3D Segmentation of Individual Plant Leaves Using a RGB-D Camera for Agricultural Automation. Sensors 2015, 15, 20463-20479.

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