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

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

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.

Key words: maize, deep learning, variety recognition, MobileNetV2, machine vision

CLC Number:

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.

References

[1] 邓立苗, 韩仲志, 于仁师. 基于机器视觉的花生品种识别系统研究[J]. 农机化研究, 2013, 35(8): 166-169.
Deng Limiao, Han Zhongzhi, Yu Renshi.Research on Peanut Species Identification System Based on Computer Vision[J]. Journal of Agricultural Mechanization Research, 2013, 35(8): 166-169.
[2] 樊超, 夏旭, 石小凤, 等. 基于优化神经网络的小麦品种分类研究[J]. 河南工业大学学报(自然科学版), 2012, 33(4): 72-76.
Fan Chao, Xia Xu, Shi Xiaofeng, et al.Research on Wheat Variety Classification Based on Optimized Artificial Neural Network[J]. Journal of Henan University of Technology (Natural Science Edition), 2012, 33(4): 72-76.
[3] 韩仲志, 杨锦忠. 多类支持向量机在玉米品种识别中的应用[J]. 农机化研究, 2010, 32(11): 159-163.
Han Zhongzhi, Yang Jinzhong.Using Multi-variable SVM Arithmetic in Maize Cultivars Classifications[J]. Journal of Agricultural Mechanization Research, 2010, 32(11): 159-163.
[4] Krizhevsky A, Sutskever I, Hinton G E.ImageNet Classification with Deep Convolutional Neural Networks[C]// Advances in Neural Information Processing Systems 25(NIPS 2012). Lake Tahoe, USA: Curran Associates Inc, 2012: 1097-1105.
[5] 徐岩, 刘林, 李中远, 等. 基于卷积神经网络的玉米品种识别[J]. 江苏农业学报, 2020, 36(1): 18-23.
Xu Yan, Liu Lin, Li Zhongyuan, et al.Recognition of Maize Varieties Based on Convolutional Neural Network[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(1): 18-23.
[6] 张芳. 基于深度学习的玉米果穗分类识别[D]. 南昌: 江西农业大学, 2019.
Zhang Fang.Classification and Identification of Corn Ear Based on Deep Learning[D]. Nanchang: Jiangxi Agricultural University, 2019.
[7] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-scale Image Recognition[EB/OL]. (2015-04-10) [2021-05-15]. https://arxiv.org/abs/1409. 1556.
[8] Sandler M, Howard A, Zhu M, et al.MobileNetV2: Inverted Residuals and Linear Bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 4510-4520.
[9] Howard A, Zhu M, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[EB/OL]. (2017-04-17) [2021-02-15]. https://arxiv.org/abs/1704.04861.
[10] Tan M X, Le Q.EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks[C]// 36th International Conference on Machine Learning. Long Beach, USA: ICML, 2019: 6105-6114.
[11] Chollet F.Xception:Deep Learning with Depthwise Separable Convolutions[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017: 1800-1807.
[12] He K M, Zhang X Y, Ren S Q, et al.Deep Residual Learning for Image Recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE, 2016: 770-778.
[13] Huang G, Liu Z, Laurens V, et al.Densely Connected Convolutional Networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE, 2017: 2261-2269.
[14] 孙旭豪, 傅中添, 严玲, 等. EfficientNet在阴虚证眼象识别中的应用研究[J]. 中医药信息, 2020, 37(3): 29-34.
Sun Xuhao, Fu Zhongtian, Yan Ling, et al.Application Research of EfficientNet on Eye Recognition of Yin Deficiency Syndrome[J]. Information on Traditional Chinese Medicine, 2020, 37(3): 29-34.
[15] 郑一力, 张露. 基于迁移学习的卷积神经网络植物叶片图像识别方法[J]. 农业机械学报, 2018, 49(增1): 354-359.
Zheng Yili, Zhang Lu.Plant Leaf Image Recognition Method Based on Transfer Learning with Convolutional Neural Networks[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(S1): 354-359.
[16] 李淼, 王敬贤, 李华龙, 等. 基于CNN和迁移学习的农作物病害识别方法研究[J]. 智慧农业, 2019, 1(3): 46-55.
Li Miao, Wang Jingxian, Li Hualong, et al.Method for Identifying Crop Disease Based on CNN and Transfer Learning[J]. Smart Agriculture, 2019, 1(3): 46-55.
[17] Kingma D, Ba J. Adam:A Method for Stochastic Optimization[J/OL]. (2017-01-30) [2021-02-15]. https://arxiv.org/abs/1412.6980.
[18] 仝卫国, 李敏霞, 张一可. 深度学习优化算法研究[J]. 计算机科学, 2018, 45(增2): 155-159.
Tong Weiguo, Li Minxia, Zhang Yike.Research on Optimization Algorithm of Deep Learning[J]. Computer Science, 2018, 45(S2): 155-159.