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Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

文献类型：期刊论文

作者Zhang, Jiajing5,6,7; Min, An4; Steffenson, Brian J.3; Su, Wen-Hao2; Hirsch, Cory D.3; Anderson, James1; Wei, Jian7; Ma, Qin7; Yang, Ce4
刊名FRONTIERS IN PLANT SCIENCE
出版日期2022-02-10
卷号13页码:13
ISSN号1664-462X
关键词wheat spike instance segmentation Hybrid Task Cascade model challenging dataset non-structural field
DOI10.3389/fpls.2022.834938
通讯作者Ma, Qin(sockline@163.com) ; Yang, Ce(ceyang@umn.edu)
英文摘要Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.
WOS关键词NETWORKS ; MACHINE
资助项目USDA-ARS United States Wheat and Barley Scab Initiative[59-0206-0-181] ; Lieberman-Okinow Endowment at the University of Minnesota ; State of Minnesota Small Grains Initiative ; Provincial Natural Science Foundation Project[ZR2021MC099]
WOS研究方向Plant Sciences
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000760819100001
资助机构USDA-ARS United States Wheat and Barley Scab Initiative ; Lieberman-Okinow Endowment at the University of Minnesota ; State of Minnesota Small Grains Initiative ; Provincial Natural Science Foundation Project
源URL[http://ir.ia.ac.cn/handle/173211/47894]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Ma, Qin; Yang, Ce
作者单位1.Univ Minnesota, Dept Agron & Plant Genet, St Paul, MN USA
2.China Agr Univ, Coll Engn, Beijing, Peoples R China
3.Univ Minnesota, Dept Plant Pathol, St Paul, MN USA
4.Univ Minnesota, Dept Bioprod & Biosyst Engn, St Paul, MN 55108 USA
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
7.China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jiajing,Min, An,Steffenson, Brian J.,et al. Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model[J]. FRONTIERS IN PLANT SCIENCE,2022,13:13.
APA Zhang, Jiajing.,Min, An.,Steffenson, Brian J..,Su, Wen-Hao.,Hirsch, Cory D..,...&Yang, Ce.(2022).Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.FRONTIERS IN PLANT SCIENCE,13,13.
MLA Zhang, Jiajing,et al."Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model".FRONTIERS IN PLANT SCIENCE 13(2022):13.

入库方式： OAI收割

来源：自动化研究所

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