Edge computing-based computer vision and deep transfer learning for high-throughput assessment of

Libin Wu, Liangliang Zhu, Haiyong Weng, Guoping Chen, Hongfei Liu, Yande Liu, Dapeng Ye

Abstract read

In one paragraph

Article in Plant phenomics (Washington, D.C.), 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

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

1 citing paper in PubMed.

Detection of Kernel-Level Spoilage Adulteration in Dried Goji Berries Using Zero-Shot Learning and Computer Vision.Foods (Basel, Switzerland) · 2026
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

7 authors.

Libin WuSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361000, PR China.

Liangliang ZhuCollege of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, PR China.

Haiyong WengCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, PR China.

Guoping ChenEconomic Crops Station, Zhangzhou, 363000, PR China.

Hongfei LiuAopu Tiancheng Optoelectronics Co., Ltd., Xiamen, 361000, PR China.

Yande LiuSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361000, PR China.

Dapeng YeCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Manual assessment of toxic fungal infection levels in crop seeds is important for developing antifungal-resistant cultivars, yet it has long been recognized as health-risking and inherently subjective. This study presents an edge computing-based computer vision approach for high-throughput on-site assessment and quantification of

Indexed as

Aspergillus flavusComputer visionCrop seedsDeep transfer learningEdge computing

Identifiers

PMID42382237

DOI10.1016/j.plaphe.2025.100110

PMCPMC13316237

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.