Evidence map›Paper›PMID 42519607›Full record

ArticleFrontiers in plant science2026

DFA-YOLO: an enhanced YOLOv11-OBB and knowledge distillation-based maize stomata detection system.

Zhenzhen Lin, Rui Liu, Antong Deng, Zhongxiang Xing, Huajuan Gao, Ziqi Yang, Yanxi Liu, Yuling Liu, Zhuolun He, Binhong Zhou and 3 more

Erratum issuedAbstract 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. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Zhenzhen LinCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Rui LiuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Antong DengCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Zhongxiang XingCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Huajuan GaoCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Ziqi YangCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Yanxi LiuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Yuling LiuCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Zhuolun HeCollege of Mechanical and Electrical Engineering, Sichuan Agricultural, University, Ya' an, Sichuan, China.
Binhong ZhouCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Jie XuMaize Research Institute, Sichuan Agricultural University, Wenjiang, Sichuan, China.
Yan GuoCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
Zhiyong LiCollege of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods: This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u Results: DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion: Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.

Indexed as

detection platformfocaler-CIoUknowledge distillationstomatal detectionYOLOv11-OBB

Identifiers

PMID42519607
PMCPMC13382967

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.