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Link to original content: https://doi.org/10.1007/s11042-022-13420-y
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Enhanced individual characteristics normalized lightweight rice-VGG16 method for rice seed defect recognition

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Abstract

The current rice seed defect recognition methods have several disadvantages, including the background segmentation of images, complex operation, and non-normalization processing. In this study, we proposed a enhanced individual characteristics normalized lightweight Rice-Visual Geometry Group Network 16(Rice-VGG16) method for rice seed defect recognition. Firstly, rice seed defects are divided, and the image processing steps are used to standardize the seed images and construct the datasets. Secondly, the fifth max-pooling layer is modified to the ave-pooling layer, and the activation function is defined as Leaky Rectified Linear Units(Leaky-ReLU) to enhance the individual characteristics and improve the recognition accuracy. Then, a batch normalization layer is added after the last convolution layer of each convolution group, the first full connection layer is removed, the node number of the second full connection layer is modified to 1024, and the model parameters are fine-tuned to carry out model lightweight. Thus the normalized lightweight Rice-VGG16 model is constructed to improve recognition speed. Experimental results with real datasets demonstrated that: the model was able to accurately identify rice seed defects, with the training accuracy of 99.63% and the recognition accuracy of 99.51%; compared with traditional VGG16 model, since the amount of training parameters has been reduced by 79.88%,the proposed method can reduce the training time per epoch and the recognition time by 18.83%,13.59%, respectively. The proposed method can be used for rice seed defect recognition or accurate grain grading and breeding. 

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Abbreviations

A :

Overall accuracy of the model

bin :

Pooled window

BN:

Batch normalization

CCD:

Charge-coupled device

CMOS:

Complementary metal oxide semiconductor

CNN:

Convolutional neural network

FC:

Full connection layers

FN :

False Negative

FP :

False Positive

HSI:

Hue-saturation-intensity

LED:

Light emitting diode

LRN:

Local response normalization

Leaky-ReLU:

Leaky Rectified Linear Units

MATLAB:

Matrix laboratory

P :

Precision

R :

Recall rate

ReLU:

Rectified linear unit

RD_SD:

Rice seed defect dataset

RGD:

Random gradient descent

S :

Specificity

TN :

True negative

TP :

True Positive

VGG16:

Visual Geometry Group Network 16

*:

Convolution

\(\lambda\) :

Parameter of LeakyReLU

γ,  β :

Model learning parameters

µ B :

Sample mean

σ B :

Sample variance

bin :

Pooled window

\({BN}_{\gamma ,\beta }\left({x}_{i}\right)\) :

Output of Batch Normalization Layer

i, j :

Size of the pooled window

m :

Number of batches

n i,j :

Number of pixels fixed in bin

p 0 :

First pixel points in the pooled window

P n :

Last pixel points in the pooled window

x :

Sum of the input layer pixels

x i :

the ith training sample

y :

Pixel of the output layer

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Funding

The authors would like to thank all the reviewers for their valuable comments. This work is supported by National Natural Science Foundation of China (Grant No. 51,475,409), Research Project of State Key Laboratory of Mechanical System and Vibration (Grant No. MSV201810), and Yangzhou city - Yangzhou University of Science and Technology Cooperation Program Funds (No. YZ2020166). 

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Authors and Affiliations

  1. College of Mechanical Engineering, Yangzhou University, Jiangsu, 225127, Yangzhou, People’s Republic of China

    Jin Sun & Yang Zhang

  2. China Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Yangzhou University, Jiangsu, 225009, Yangzhou, China

    Jin Sun, Yang Zhang & Xinglong Zhu

  3. Department of Informatics, University of Leicester, LE1 7RH, Leicester, UK

    Jin Sun & Yu-Dong Zhang

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  1. Jin Sun
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Correspondence to Jin Sun.

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Sun, J., Zhang, Y., Zhu, X. et al. Enhanced individual characteristics normalized lightweight rice-VGG16 method for rice seed defect recognition. Multimed Tools Appl 82, 3953–3972 (2023). https://doi.org/10.1007/s11042-022-13420-y

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