Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks

Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks

This study introduces a novel approach for predicting the decay levels of potato by integrating an electronic nose system combined with feature-optimized deep learning models. The electronic nose system was utilized to collect volatile gas data from potatoes at different decay stages, offering a non...

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Bibliographic Details
Main Authors: Jiayu Mai, Haonan Lin, Xuezhen Hong, Zhenbo Wei
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Chemosensors
Subjects:
electronic nose system
potato decay prediction
feature-optimized deep learning
gaussian mixture embedded GAN (GMEGAN)
data augmentation
channel attention modules
Online Access:https://www.mdpi.com/2227-9040/12/12/275
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