| A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation | |
Zhang, Liankuan1; Xia, Chunlei2,3 ; Xiao, Deqin1; Weckler, Paul4; Lan, Yubin5; Lee, Jang M.3
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| 发表期刊 | BIOSYSTEMS ENGINEERING
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| ISSN | 1537-5110 |
| 2021-06-01 | |
| 卷号 | 206页码:94-108 |
| 关键词 | Leaf extraction Leaf pose measurement Leaf shape estimation Occlusion detection Active shape model Plant image analysis |
| DOI | 10.1016/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.cn/handle/133337/29325 |
| 专题 | 中国科学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 中国科学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 7714 | Zhang, 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. |
| APA | Zhang, 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. |
| MLA | Zhang, 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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| A coarse-to-fine lea（3215KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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