| Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model | |
Zhang, Jiajing1,2,3 ; Min, An4; Steffenson, Brian J.5; Su, Wen-Hao6; Hirsch, Cory D.5; Anderson, James7; Wei, Jian1; Ma, Qin1; Yang, Ce4
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| Source Publication | FRONTIERS IN PLANT SCIENCE
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| ISSN | 1664-462X |
| 2022-02-10 | |
| Volume | 13Pages:13 |
| Corresponding Author | Ma, Qin(sockline@163.com) ; Yang, Ce(ceyang@umn.edu) |
| Abstract | 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. |
| Keyword | wheat spike instance segmentation Hybrid Task Cascade model challenging dataset non-structural field |
| DOI | 10.3389/fpls.2022.834938 |
| WOS Keyword | NETWORKS ; MACHINE |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | Plant Sciences |
| WOS Subject | Plant Sciences |
| WOS ID | WOS:000760819100001 |
| Publisher | FRONTIERS MEDIA SA |
| Sub direction classification | 目标检测、跟踪与识别 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/47895 |
| Collection | 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 |
| Corresponding Author | Ma, Qin; Yang, Ce |
| Affiliation | 1.China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Univ Minnesota, Dept Bioprod & Biosyst Engn, St Paul, MN 55108 USA 5.Univ Minnesota, Dept Plant Pathol, St Paul, MN USA 6.China Agr Univ, Coll Engn, Beijing, Peoples R China 7.Univ Minnesota, Dept Agron & Plant Genet, St Paul, MN USA |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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. |
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