This work is licensed under the Creative Commons Attribution 4.0 International License.
Yanming Guo; Yu Liu; Erwin M. Bakker; Yuanhao Guo; Michael S. Lew. “CNN-RNN: a large-scale hierarchical image classification framework”, 2017/12/12, Volume 77, Issue 8GuoYanmingLiuYuBakkerErwin M.GuoYuanhaoLewMichael S.2017/12/1277810.1007/s11042-017-5443-xSearch in Google Scholar
C. S. Nandi, B. Tudu, C. Koley. “An automated machine vision based system for fruit sorting and grading”, 2012 Sixth International Conference on Sensing Technology (ICST), 2012NandiC. S.TuduB.KoleyC.2012 Sixth International Conference on Sensing Technology (ICST)201210.1109/ICSensT.2012.6461669Search in Google Scholar
Li X X, Zhang L X, et al. The corn disease remote diagnostic system in China [J]. Journal of Food Agriculture and Environment, 2012, 10(1).LiX XZhangL XThe corn disease remote diagnostic system in China[J]2012101Search in Google Scholar
Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors [J]. 1986, 323(6088)RumelhartD EHintonG EWilliamsR J[J]1986323608810.1038/323533a0Search in Google Scholar
Kezhu Tan, Shuwen Wang, Yuzhu Song, Yao Liu, Zhenping Gong. “Estimating nitrogen status of rice canopy using hyperspectral reflectance combined with BPSO-SVR in cold region”, Chemometrics and Intelligent Laboratory Systems, 2018TanKezhuWangShuwenSongYuzhuLiuYaoGongZhenping“Estimating nitrogen status of rice canopy using hyperspectral reflectance combined with BPSO-SVR in cold region”201810.1016/j.chemolab.2017.11.014Search in Google Scholar
Zhenyu Lu, Bin Xu, Le Sun, Tianming Zhan, Songze Tang. “3D Channel and Spatial Attention Based Multi-Scale Spatial Spectral Residual Network for Hyperspectral Image Classification”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020LuZhenyuXuBinSunLeZhanTianmingTangSongze“3D Channel and Spatial Attention Based Multi-Scale Spatial Spectral Residual Network for Hyperspectral Image Classification”2020Search in Google Scholar
Daral N. Histograms of Oriented Gradients for Human Detection [J]. Proc. of CVPR, 2005.DaralN.Histograms of Oriented Gradients for Human Detection[J]2005Search in Google Scholar
Xu L, Xu X, Hu M, et al. Corn leaf disease identification based on multiple classifiers fusion [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015.XuLXuXHuMCorn leaf disease identification based on multiple classifiers fusion[J]2015Search in Google Scholar
Yunhao Shi, Hua Xu, Lei Jiang, Yinghui Liu. “Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training”, IEEE Access, 2020.ShiYunhaoXuHuaJiangLeiLiuYinghui“Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training”202010.1109/ACCESS.2020.3012712Search in Google Scholar
Guo Y, Liu Y, Oerlemans A, et al. Deep learning for visual understanding: A review [J]. Neurocomputing, 2016, 187(C):27–48.GuoYLiuYOerlemansADeep learning for visual understanding: A review[J]2016187C274810.1016/j.neucom.2015.09.116Search in Google Scholar
Dechant C, Wiesner-Hanks T, et al. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning [J]. Phytopathology, 2017, 107(11):1426–1432.DechantCWiesner-HanksTAutomated identification of northern leaf blight-infected maize plants from field imagery using deep learning[J]2017107111426143210.1094/PHYTO-11-16-0417-R28653579Search in Google Scholar
Qiang Guo, Xin Yu, Guoqing Ruan. “LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning”, Symmetry, 2019.GuoQiangYuXinRuanGuoqing“LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning”201910.3390/sym11040540Search in Google Scholar
Yang Yang, Jinbao Duan, Haitao Yu, Zhipeng Gao, Xuesong Qiu. “An Image Classification Method Based on Deep Neural Network with6 Energy Model”, Computer Modeling in Engineering and Sciences, 2018.YangYangDuanJinbaoYuHaitaoGaoZhipengQiuXuesong“An Image Classification Method Based on Deep Neural Network with6 Energy Model”201810.31614/cmes.2018.04249Search in Google Scholar
Toto Haryanto, Ito Wasito, Heru Suhartanto. ““Convolutional Neural Network (CNN) for gland images classification”, 2017 11th International Conference on Information and Communication Technology and System (ICTS), 2017.HaryantoTotoWasitoItoSuhartantoHeru2017 11th International Conference on Information and Communication Technology and System (ICTS)201710.1109/ICTS.2017.8265646Search in Google Scholar
Kang, Chen. “Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards”, Sensors, 2019KangChen“Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards”201910.3390/s19204599683230631652634Search in Google Scholar
Lixin Liao, Yao Zhao, Shikui Wei, Jingdong Wang, Ruoyu Liu. “Finding the Secret of CNN Parameter Layout under Strict Size Constraint”, Proceedings of the 2017 ACM on Multimedia Conference - MM ’17, 2017.LiaoLixinZhaoYaoWeiShikuiWangJingdongLiuRuoyuProceedings of the 2017 ACM on Multimedia Conference - MM ’17201710.1145/3123266.3123346Search in Google Scholar
Chunyao Fang, Kebin Jia, Pengyu Liu, Liang Zhang. “Research on Cloud Recognition Technology Based on Transfer Learning”, 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019.FangChunyaoJiaKebinLiuPengyuZhangLiang2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)201910.1109/APSIPAASC47483.2019.9023267Search in Google Scholar
Kai Yang, Zhiyi Sun, Anhong Wang, Ruizhen Liu, Qianlai Sun, Yin Wang. “Deep hashing network for material defect image classification”, IET Computer Vision, 2018.YangKaiSunZhiyiWangAnhongLiuRuizhenSunQianlaiWangYin“Deep hashing network for material defect image classification”201810.1049/iet-cvi.2018.5286Search in Google Scholar
Yining Meng, Guoyin Wang, Qun Liu. “Multi-layer Convolutional Neural Network Model12 Based on Prior Knowledge of Knowledge Graph for Text Classification”, 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2019.MengYiningWangGuoyinLiuQun2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)201910.1109/ICCCBDA.2019.8725669Search in Google Scholar
Xu L, Xu X, Hu M, et al. Corn leaf disease identification based on multiple classifiers fusion [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(14):194–201.XuLXuXHuMCorn leaf disease identification based on multiple classifiers fusion[J]20153114194201Search in Google Scholar
Zhao Q I, Zhaohui J, Chunhe Y, et al. Identification of maize leaf diseases based on image technology [J]. Journal of Anhui Agricultural University, 2016, 43(2):325–330.ZhaoQ IZhaohuiJChunheYIdentification of maize leaf diseases based on image technology[J]2016432325330Search in Google Scholar
Kai S, Xiaoyan S, Jianwei J. Corn leaf disease recognition based on support vector machine method [J]. Transactions of the Chinese Society of Agricultural Engineering, 2007, 23(1):155–157.KaiSXiaoyanSJianweiJCorn leaf disease recognition based on support vector machine method[J]2007231155157Search in Google Scholar
Li C, Lanying W. Research on Application of Probability Neural Network in Maize Leaf Disease Identification [J]. Journal of Agricultural Mechanization Research, 2011, 33(6):145–148.LiCLanyingWResearch on Application of Probability Neural Network in Maize Leaf Disease Identification[J]2011336145148Search in Google Scholar