Evidence map›Paper›PMID 38501134›Full record

ArticleFrontiers in plant science2024

Moisture content online detection system based on multi-sensor fusion and convolutional neural network.

Taoqing Yang, Xia Zheng, Hongwei Xiao, Chunhui Shan, Jikai Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2024. 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.

  1. Review
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

5 authors.

Taoqing YangCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
Xia ZhengCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
Hongwei XiaoCollege of Engineering, China Agricultural University, Beijing, China.
Chunhui ShanCollege of Food, Shihezi University, Shihezi, China.
Jikai ZhangCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model's predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (

Indexed as

convolutional neural networkmoisture contentmulti-sensor fusiononline detectionprediction model

Identifiers

PMID38501134
PMCPMC10944943

What OpenQuestion holds

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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.