Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

10.1K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
10.1K
Deconvolution01:20

Deconvolution

718
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
718
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

507
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
507
Moisture Content and Bulking of Aggregate01:10

Moisture Content and Bulking of Aggregate

618
The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
When aggregates are exposed to rain or sit in stockpiles, they absorb moisture, which must be...
618

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Low-molecular-weight polysaccharide and polyol impregnation enhances the rehydration recovery capacity of freeze-dried potato slices.

Food chemistry: X·2026
Same author

The NAC Transcription Factor SlNAP2 Enhances Tomato Resistance to Ralstonia solanacearum.

Physiologia plantarum·2026
Same author

Multilayer regulation of CRISPR systems: integrating anti-CRISPR proteins, CRISPRi/a, and quorum sensing networks.

Frontiers in cellular and infection microbiology·2026
Same author

Hydroxypropylated/oxidized starch-based regulation of the potato-barley system for extrusion-based 3D food printing.

International journal of biological macromolecules·2026
Same author

Patient preferences and willingness-to-pay for therapy in generalized myasthenia gravis: a large-scale discrete choice experiment in China.

Frontiers in immunology·2026
Same author

TRIDENT: A multi-task, triple-branch deep learning framework for EEG-based recognition, severity estimation, and future high-anger prediction in an on-road Wizard-of-Oz paradigm.

Accident; analysis and prevention·2026

Related Experiment Video

Updated: Apr 12, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.6K

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

Taoqing Yang1,2,3, Xia Zheng1,2,3, Hongwei Xiao4

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.

Frontiers in Plant Science
|March 19, 2024
PubMed
Summary

This study developed a real-time moisture content detection system for agricultural products using multi-sensor fusion and a convolutional neural network (CNN). The CNN model significantly outperformed other methods, enabling intelligent drying equipment development.

Keywords:
convolutional neural networkmoisture contentmulti-sensor fusiononline detectionprediction model

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

531
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Related Experiment Videos

Last Updated: Apr 12, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

6.6K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

531
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Area of Science:

  • Agricultural Engineering
  • Machine Learning
  • Sensors and Instrumentation

Background:

  • Real-time monitoring of agricultural product moisture content during drying is crucial for quality control and process optimization.
  • Traditional methods often lack the precision and speed required for dynamic drying processes.
  • Intelligent development of drying equipment necessitates advanced online detection capabilities.

Purpose of the Study:

  • To develop and validate a novel system for online moisture content detection in agricultural products during drying.
  • To apply multi-sensor fusion combined with a convolutional neural network (CNN) for enhanced predictive accuracy.
  • To compare the performance of the CNN model against traditional regression models like PLSR and SVM.

Main Methods:

  • A multi-sensor data acquisition platform was constructed, collecting data from load, air velocity, and temperature sensors, along with tray position.
  • A convolutional neural network (CNN) prediction model was established using sensor data as input to predict material weight, a proxy for moisture content.
  • The CNN model's predictive performance was rigorously compared with linear partial least squares regression (PLSR) and support vector machine (SVM) models.

Main Results:

  • The CNN prediction model demonstrated superior performance with a determination coefficient (R) of 0.9989 and root mean square error (RMSE) of 6.9.
  • Validation experiments confirmed the system's effectiveness, achieving an R of 0.9901 and RMSE of 1.47 for online moisture content detection.
  • The developed system significantly outperformed PLSR and SVM models in accuracy and reliability for agricultural product drying monitoring.

Conclusions:

  • The integration of multi-sensor fusion and CNN provides a highly accurate and reliable method for online moisture content detection in agricultural products.
  • The developed system meets the stringent requirements for real-time monitoring during the drying process.
  • This research offers significant implications for advancing drying process research, intelligent drying equipment, and online detection of other agricultural product parameters.