Zhang, Liankuan1; Xia, Chunlei2,3
; Xiao, Deqin1; Weckler, Paul4; Lan, Yubin5; Lee, Jang M.3发表期刊BIOSYSTEMS ENGINEERING
ISSN1537-5110
2021-06-01
卷号206页码:94-108
关键词Leaf extractionLeaf pose measurementLeaf shape estimationOcclusion detectionActive shape modelPlant image analysis
DOI10.1016/j.biosystemseng.2021.03.017
通讯作者Xia, Chunlei(clxia@yic.ac.cn); Lee, Jang M.(jmlee@pusan.ac.kr)
英文摘要Plant leaf detection and segmentation are challenging tasks for in-situ plant image analysis. Here, a novel leaf detection scheme is proposed to detect individual leaves and accurately determine leaf shapes in natural scenes. A leaf skeleton-extraction method was developed by analysing local image features of skeleton pixels. Approximate positions of individual leaves were determined according to the main leaf skeleton. Sub-images containing only single target leaves were extracted from whole plant images according to position and size of the main skeleton. Accurate leaf analysis was conducted on the sub-images of individual leaves. Leaf direction was calculated by examining the structure of the main leaf skeleton. Joint segmentation by combining region and active shape model was presented to accurately elucidate leaf shape. Leaf detection was implemented using deep learning approach, Faster R-CNN. A plant leaf image dataset containing four types of leaf images of different complexity was built to evaluate detection algorithms. Plant leaves with occlusions and complex backgrounds were effectively detected and their shapes accurately determined. Detection accuracy of the proposed method was 81.10%-100%, and 86.75%-100% for Faster R-CNN. The method demonstrated a comparable detection ability to that of Faster R-CNN. Furthermore, the rates of success to determine leaf direction by our method ranged between 89.06% and 100%, while the average measurement difference was 1.29 degrees compared with manual measurement. The accuracy of shape measurement was 75.95%-100% for all types of plant images. Therefore, this method is accurate and stable for precise leaf measurements in agricultural applications. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.
资助机构Key Area Research and Development Program of Guangdong Province; CAS Key Technology Talent Program; Key Research and Development Program of Yantai; China Scholarship Council (CSC); BK21PLUS, Creative Human Resource Development Program for IT Convergence
收录类别SCI
语种英语
关键词[WOS]ACTIVE SHAPE MODELS; CLASSIFICATION; IMAGE
研究领域[WOS]Agriculture
WOS记录号WOS:000651462400008
引用统计
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/29325
专题中科院海岸带环境过程与生态修复重点实验室_海岸带环境工程技术研究与发展中心
中科院海岸带环境过程与生态修复重点实验室
通讯作者Xia, Chunlei; Lee, Jang M.作者单位1.South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China
2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
3.Pusan Natl Univ, Dept Elect Engn, Busan 46241, South Korea
4.Oklahoma State Univ, Dept Biosyst & Agr Engn, Stillwater, OK 74078 USA
5.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China
推荐引用方式
GB/T 7714Zhang, Liankuan,Xia, Chunlei,Xiao, Deqin,et al. A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation[J]. BIOSYSTEMS ENGINEERING,2021,206:94-108.
APAZhang, Liankuan,Xia, Chunlei,Xiao, Deqin,Weckler, Paul,Lan, Yubin,&Lee, Jang M..(2021).A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation.BIOSYSTEMS ENGINEERING,206,94-108.
MLAZhang, Liankuan,et al."A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation".BIOSYSTEMS ENGINEERING 206(2021):94-108.
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