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

Sensors

Chunlei Xia³  Longtan Wang¹  Bu-Keun Chung²  Jang-Myung Lee¹ 

[1] School of Electrical Engineering, Pusan National University, Busan 609-735, Korea; E-Mail:;Division of Plant Environment, Gyeongsangnam-Do Agricultural Research and Extension Services, Jinju 660-985, Korea; E-Mail:;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; E-Mail:

关键词: plant monitoring;    occlusions;    leaf detection;    mean shift;    center of divergence;    automatic initialization;   

DOI  :  10.3390/s150820463

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.