Evidence mapPaperPMID 41519826Full record

ArticleNPJ science of food2026

Non-destructive detection of micro-impurities in tea using the YOLOv11-PFT model.

Zejun Wang, Chun Wang, Wenxia Yuan, Xiujuan Deng, Houqiao Wang, Tianyu Wu, Jinyan Zhao, Weihao Liu, Baijuan Wang

Abstract read
In one paragraph

Article in NPJ science of food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zejun WangCollege of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China.
Chun WangCollege of Tea Science, Yunnan Agricultural University, Kunming, China.
Wenxia YuanCollege of Tea Science, Yunnan Agricultural University, Kunming, China.
Xiujuan DengCollege of Tea Science, Yunnan Agricultural University, Kunming, China.
Houqiao WangState Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, School of Agriculture, Yunnan University, Kunming, China.
Tianyu WuCollege of Tea Science, Yunnan Agricultural University, Kunming, China.
Jinyan ZhaoCollege of Tea Science, Yunnan Agricultural University, Kunming, China.
Weihao LiuCollege of Tea Science, Yunnan Agricultural University, Kunming, China.
Baijuan WangCollege of Tea Science, Yunnan Agricultural University, Kunming, China. wangbaijuan2023@163.com.

Funding

Menghai County Smart Tea Industry Science and Technology Special Mission Team of Yunnan Province No. 202304BI090013National Natural Science Foundation of China No. 32460782Sub - project of Yunnan Major Science and Technology Special Program No. 202302AE09002001Yunnan Basic Research Special Fund No. 202301AS070083Yunnan International Joint Laboratory for Intelligent Tea Industry No. 202403AP140022Yunnan Patent Innovation and Transformation Talent "Introduction, Cultivation and Use" Project No. YNZCZH2024001Yunnan Tea Industry Artificial Intelligence and Big Datacometing inter Application Innovation Team No. 202405AS350025
6 · The paper itself

Abstract

Microscopic impurities can contaminate tea during production, processing, and packaging. Current technologies remove only visible contaminants, leaving microscopic foreign objects that compromise tea quality, and reliable detection methods remain lacking. To address this challenge, we propose YOLOv11-PFT, an improved deep learning model based on YOLOv11, enhanced with Powerful-IoU loss, FasterNet, and Triple Attention modules to boost detection accuracy, reduce model size, and improve feature extraction. The resulting lightweight model achieves 99.16% detection accuracy for microscopic tea contaminants, with Precision, Recall, F

Identifiers

PMID41519826
PMCPMC12894953

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.