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Related Experiment Video

Updated: May 22, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

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Published on: January 30, 2019

Early apple moldy core classification via multi-modal sensing and SE-ResNet18.

Ruifeng Wang¹, Ninghua Zhu¹, Xuexia Ma¹

¹College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|May 20, 2026

Summary

This study introduces a new method using Visible-Near Infrared Spectroscopy and Electronic Nose data to detect apple moldy core disease early. The multimodal approach achieved 95.93% accuracy, improving non-destructive diagnosis for agricultural products.

Keywords:

Attention mechanism Deep learning Early-stage apple moldy core disease Electronic nose Visible-near infrared spectroscopy

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Last Updated: May 22, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

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Published on: January 30, 2019

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

Area of Science:

Agricultural Science
Computer Vision
Spectroscopy

Background:

Apple moldy core disease significantly impacts postharvest quality.
Early internal lesions are undetectable by visual inspection, necessitating advanced diagnostic methods.

Purpose of the Study:

To develop an efficient, non-destructive method for early diagnosis of apple moldy core disease.
To fuse Visible-Near Infrared Spectroscopy (Vis-NIR) and Electronic Nose (E-nose) data for improved disease classification.

Main Methods:

1D time-series and spectral data were transformed into image representations using Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP) techniques.
A two-branch SE-ResNet18 deep learning model with a channel attention mechanism was employed for multimodal fusion and classification.
The SE attention module was utilized for feature calibration and modal balance.

Main Results:

The multimodal fusion model achieved a classification accuracy of 95.93%, outperforming single-modal approaches.
Ablation studies confirmed the critical role of the SE attention module in enhancing feature representation and modal balance.
Information entropy analysis demonstrated improved discriminability of features extracted by the proposed method.

Conclusions:

Multimodal data fusion effectively complements information from different sources for accurate disease detection.
The proposed method offers a reliable and promising solution for the non-destructive detection of early diseases in agricultural products.
This approach has significant potential for practical application and widespread adoption in the agricultural industry.