Variety Recognition Based on Deep Learning and Double-Sided Characteristics of Maize Kernel

Authors

Feng Xiao, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;2. Henan Engineering and Technology Research Center for Intelligent Agriculture, Zhengzhou 450002, China; ;
Zhang Hui, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;2. Henan Engineering and Technology Research Center for Intelligent Agriculture, Zhengzhou 450002, China; ;
Zhou Rui, 3. Institute of Agricultural Science and Technology Information, Chongqing Academy of Agricultural Sciences, Chongqing 401329, China;
Qiao Lu, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;
Wei Dong, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;
Dandan Li, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;
Yuyao Zhang, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;
Guoqing Zheng, 1. Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China; ;2. Henan Engineering and Technology Research Center for Intelligent Agriculture, Zhengzhou 450002, China; ;

Abstract

Abstract: In order to construct a maize kernel variety recognition model with high recognition accuracy and suitable for mobile phone application, a mobile phone is used to obtain maize kernel double-sided (embryonic and non-embryonic) images. Based on the lightweight convolutional neural network MobileNetV2 and transfer learning, a maize kernel image variety recognition model is constructed. In view of the existing research methods are mainly for single-sided recognition of maize kernel variety, the performance of single-sided and double-sided characteristics modeling and recognition is compared. The results show that the double-sided recognition accuracy of maize kernel double-sided characteristics modeling is 99.83%, which is better than single-sided characteristics modeling and recognition. It is also better than double-sided recognition after modeling embryonic side and non-embryonic side images respectively. It is suitable for the application demand of maize kernel variety recognition on mobile phone.

Recommended Citation

Xiao, Feng; Hui, Zhang; Rui, Zhou; Lu, Qiao; Dong, Wei; Li, Dandan; Zhang, Yuyao; and Zheng, Guoqing (2022) "Variety Recognition Based on Deep Learning and Double-Sided Characteristics of Maize Kernel," Journal of System Simulation: Vol. 33: Iss. 12, Article 23.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0771
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss12/23

First Page

2983

Revised Date

2021-07-29

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-FZ0771

Last Page

2991

CLC

TP183;TP391.9

Recommended Citation

Feng Xiao, Zhang Hui, Zhou Rui, Qiao Lu, Wei Dong, Li Dandan, Zhang Yuyao, Zheng Guoqing. Variety Recognition Based on Deep Learning and Double-Sided Characteristics of Maize Kernel[J]. Journal of System Simulation, 2021, 33(12): 2983-2991.

Corresponding Author

Guoqing Zheng,

DOI

10.16182/j.issn1004731x.joss.21-FZ0771