Detection and Classification of Early Decay on Blueberry Based on Improved Deep Residual 3D Convolutional Neural Network in Hyperspectral Images

Recently, the automatic detection of decayed blueberries is still a challenge in food industry. Early decay of blueberries happens on surface peel, which may adopt the feasibility of hyperspectral imaging mode to detect decayed region of blueberries. An improved deep residual 3D convolutional neural...

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Bibliographic Details
Published inScientific programming Vol. 2020; no. 2020; pp. 1 - 12
Main Authors Pei, Zhili, Zhang, Jun, Wang, Qinghu, Qiao, Shicheng
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Subjects
Accuracy
Algorithms
Artificial neural networks
Berries
Blueberries
Classification
Decay
Discriminant analysis
Feature extraction
Food processing industry
Fruits
Hyperspectral imaging
Image classification
Image detection
Image enhancement
Methods
Neural networks
Nondestructive testing
Optimization
Parameters
Support vector machines
Training
Vision systems
Wavelet transforms
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Summary:Recently, the automatic detection of decayed blueberries is still a challenge in food industry. Early decay of blueberries happens on surface peel, which may adopt the feasibility of hyperspectral imaging mode to detect decayed region of blueberries. An improved deep residual 3D convolutional neural network (3D-CNN) framework is proposed for hyperspectral images classification so as to realize fast training, classification, and parameter optimization. Rich spectral and spatial features can be rapidly extracted from samples of complete hyperspectral images using our proposed network. This combines the tree structured Parzen estimator (TPE) adaptively and selects the super parameters to optimize the network performance. In addition, aiming at the problem of few samples, this paper proposes a novel strategy to enhance the hyperspectral image sample data, which can improve the training effect. Experimental results on the standard hyperspectral blueberry datasets show that the proposed framework improves the classification accuracy compared with AlexNet and GoogleNet. In addition, our proposed network reduces the number of parameters by half and the training time by about 10%.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2020/8895875