Evidence map›Paper›PMID 42582468›Full record

ArticleFrontiers in plant science2026

UNet-ECA-Bio: a biologically informed deep learning model for high-throughput micro-phenotyping of rice stem vascular bundles.

Jianguo Li, Xiaoying Zhu, Zesheng Wei, Xiaoti Huang, Mingchong Yang, Dandan He, Jiahua Shi, Haoran Li, Huan Wang, Jiada Huang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Jianguo LiGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Xiaoying ZhuGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Zesheng WeiGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Xiaoti HuangGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Mingchong YangGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Dandan HeGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Jiahua ShiQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
Haoran LiSchool of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia.
Huan WangSchool of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia.
Jiada HuangGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.
Jun ShenSchool of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia.
Lingqiang WangGuangxi Sugarcane Bio-breeding Laboratory, State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Agriculture, Guangxi University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rice stem internal structure is a critical micro-phenotype influencing lodging resistance and yield; however, its analysis remains constrained by labor-intensive manual methods. Here, we present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated. Five deep learning architectures were systematically evaluated, among which UNet-VGG16 achieved the best performance with a mean intersection over union (mIoU) of 87.4% (82.49% for LVBs and 74.41% for SVBs). An improved model, UNet-ECA-Bio, further raised mIoU to 89.32% and SVB IoU to 78.97% by integrating Efficient Channel Attention (ECA) and biologically informed class weighting using an image-level dataset. Leveraging these high-accuracy phenotypic predictions, genome-wide association studies (GWAS) indicated concordance between annotated and predicted traits, with SNP overlap rates of 96% (LVB count: 1,217/1,262), 43% (SVB count: 6/14), 98% (stem area: 122/124), and 100% (cavity area: 3/3) at -log10(p) ≥ 6. Meanwhile, compared with manual annotation (estimated 10-30 minutes per image), the proposed approach processed all 686 images within 10 minutes, representing a >600-fold increase in throughput. We further developed a user-friendly software tool, "Rice_Stem_Pre_V1.1.exe," for automated phenotyping of 14 stem traits, providing a cost-effective platform for genetic studies of lodging resistance and yield improvement.

Indexed as

deep learningGWASmicro-phenotyperice stemvascular bundles

Identifiers

PMID42582468
PMCPMC13457637

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