Evidence map›Paper›PMID 37332705›Full record

ArticleFrontiers in plant science2023

A Vis/NIR spectra-based approach for identifying bananas infected with

Xuan Chu, Kun Zhang, Hongyu Wei, Zhiyu Ma, Han Fu, Pu Miao, Hongzhe Jiang, Hongli Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

8 authors.

Xuan ChuCollege of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Kun ZhangCollege of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Hongyu WeiCollege of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Zhiyu MaCollege of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Han FuCollege of Engineering, South China Agricultural University, Guangzhou, China.
Pu MiaoCollege of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Hongzhe JiangCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Hongli LiuCollege of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Methods: This study presented an approach for tracking growth and identifying different infection stages of the Results: The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively. Discussion: These results indicate the feasibility of identifying banana fruit infected with

Indexed as

banana fruitColletotrichum musae infectiondeep learning algorithmsfungi contamination detectiontraditional classification methodsVis/NIR spectra

Identifiers

PMID37332705
PMCPMC10272841

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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 OpenQuestion graph.