基于深度学习和籽粒双面特征的玉米品种识别

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

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (12): 2983-2991.doi: 10.16182/j.issn1004731x.joss.21-FZ0771

• 仿真模型/系统置信度评估技术 • 上一篇下一篇

基于深度学习和籽粒双面特征的玉米品种识别

冯晓^1,2, 张辉^1,2, 周蕊³, 乔璐¹, 魏东¹, 李丹丹¹, 张玉尧¹, 郑国清^1,2,*

1.河南省农业科学院农业经济与信息研究所,河南郑州 450002;
2.河南省智慧农业工程技术研究中心,河南郑州 450002;
3.重庆市农业科学院农业科技信息研究所,重庆 401329

收稿日期:2021-06-10 修回日期:2021-07-29 出版日期:2021-12-18 发布日期:2022-01-13
通讯作者: 郑国清(1964-),男,博士,研究员,研究方向为农业信息技术。E-mail：zgqzx@hnagri.org.cn
作者简介:冯晓(1978-),女,硕士,副研究员,研究方向为农业数据分析及图像处理。E-mail：308564967@qq.com
基金资助:
河南省科技攻关计划(212102110220); 河南省农业科学院创新团队(2021TD11)

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

Feng Xiao^1,2, Zhang Hui^1,2, Zhou Rui³, Qiao Lu¹, Wei Dong¹, Li Dandan¹, Zhang Yuyao¹, Zheng Guoqing^1,2,*

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;
3. Institute of Agricultural Science and Technology Information, Chongqing Academy of Agricultural Sciences, Chongqing 401329, China

Received:2021-06-10 Revised:2021-07-29 Online:2021-12-18 Published:2022-01-13

摘要/Abstract

摘要： 为构建高识别准确率且适用于手机端应用的玉米籽粒品种识别模型,提出利用手机获取玉米粒籽双面(胚面和非胚面)图像,基于轻量级卷积神经网络MobileNetV2和迁移学习构建玉米籽粒图像品种识别模型,针对已有研究中多以玉米籽粒单面识别为主,分析对比玉米籽粒单、双面特征建模及识别性能。结果表明,玉米籽粒双面特征建模的双面识别准确率达99.83%,优于单面特征建模识别以及胚面和非胚面图像分别建模后双面识别,适用于手机端玉米籽粒品种识别应用需求。

关键词: 玉米, 深度学习, 品种识别, MobileNetV2, 机器视觉

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

中图分类号:

冯晓, 张辉, 周蕊, 乔璐, 魏东, 李丹丹, 张玉尧, 郑国清. 基于深度学习和籽粒双面特征的玉米品种识别[J]. 系统仿真学报, 2021, 33(12): 2983-2991.

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.

参考文献

[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.

基于深度学习和籽粒双面特征的玉米品种识别

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	袁彬淦, 钟铭恩, 倪晶鑫. 线束端子压接后外观缺陷的视觉检测算法研究[J]. 系统仿真学报, 2022, 34(5): 1152-1159.
[2]	康旭, 张晓峰. 基于生成对抗神经网络的雷达遥感数据增广方法[J]. 系统仿真学报, 2022, 34(4): 920-927.
[3]	林硕, 安磊, 高治军, 单丹, 尚文利. 结合栈式自编码及长短时记忆的入侵检测研究[J]. 系统仿真学报, 2021, 33(6): 1288-1296.
[4]	程文聪, 史小康, 王志刚. 基于生成对抗网络的仿真卫星云图生成方法[J]. 系统仿真学报, 2021, 33(6): 1297-1306.
[5]	董书琴, 张斌. 面向不平衡数据的网络流量异常检测方法[J]. 系统仿真学报, 2021, 33(3): 679-689.
[6]	王霄汉, 张霖, 任磊, 谢堃钰, 王昆玉, 叶飞, 陈真. 基于强化学习的车间调度问题研究简述[J]. 系统仿真学报, 2021, 33(12): 2782-2791.
[7]	王步维, 王敏, 范谦, 王雅男, 章涵文, 乐云亮. 基于深度学习的晶体性质预测研究[J]. 系统仿真学报, 2021, 33(12): 2854-2863.
[8]	杜金莲, 李淑飞, 金雪云. 三维烟雾流场超分辨率数据生成网络模型的研究[J]. 系统仿真学报, 2021, 33(10): 2381-2389.
[9]	刘瑞军, 王向上, 张晨, 章博华. 基于深度学习的视觉SLAM综述[J]. 系统仿真学报, 2020, 32(7): 1244-1256.
[10]	阴敬方, 朱登明, 石敏, 王兆其. 基于引导对抗网络的人体深度图像修补方法[J]. 系统仿真学报, 2020, 32(7): 1312-1321.
[11]	戢晓峰, 戈艺澄. 基于深度学习的节假日高速公路交通流预测方法[J]. 系统仿真学报, 2020, 32(6): 1164-1171.
[12]	孔锐, 谢玮, 雷泰. 基于神经网络的图像描述方法研究[J]. 系统仿真学报, 2020, 32(4): 601-611.
[13]	秦胜伟, 李重, 李金锋, 陈梓浩, 丁靖骞, 刘万顺. 校园漫游互动AR系统设计与实现[J]. 系统仿真学报, 2019, 31(7): 1367-1376.
[14]	叶继华, 时淑霞, 李汉曦, 王仕民, 杨思渝. 基于深度学习的驾驶关注区域检测研究与实现[J]. 系统仿真学报, 2019, 31(7): 1421-1428.
[15]	张青, 邹湘军, 林桂潮, 孙艳辉. 草莓重量和形状图像特征提取与在线分级方法[J]. 系统仿真学报, 2019, 31(1): 7-9.