·924· 智能系统学报 第15卷 ing extracts and learns leaf features for plant classifica er with convolutions[Cl//Proceedings of 2015 IEEE Con- tion[J].Pattern recognition,2017,71:1-13. ference on Computer Vision and Pattern Recognition.Bo- [11]GRINBLAT G L,UZAL L C,LARESE M G,et al.Deep ston,USA,2015:1-9. learning for plant identification using vein morphological [20]HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. patterns[J].Computers and electronics in agriculture, Deep residual learning for image recognition[C]//2016 2016.127:418-424. IEEE Conference on Computer Vision and Pattern Recog- [12]PAWARA P,OKAFOR E,SURINTA O,et al.Compar- nition.Las Vegas,USA,2016:770-778. ing local descriptors and bags of visual words to deep [21]胡越，罗东阳，花奎，等.关于深度学习的综述与讨 convolutional neural networks for plant recognition[C]// 论).智能系统学报2019,14(1)1-19 6th International Conference on Pattern recognition Ap- HU Yue,LUO Dongyang,HUA Kui,et al.Overview on plications and Methods.Porto,Portugal,2017:479-486 deep learning[J].CAAI transactions on intelligent sys- [13]LIN Lihui,LI C,YANG Sheng,et al.Automated classi- tems,2019,14(1)y1-19. fication of Wuyi rock tealeaves based on support vector [22]庄福振，罗平，何清，等.迁移学习研究进展).软件学 machine[J].Concurrency and computation:practice and 报，2015,26(1)：26-39. experience,2019,31(23):e4519. [14]PANDOLFI C.MUGNAI S,AZZARELLO E,et al.Arti- ZHUANG Fuzhen,LUO Ping,HE Qing,et al.Survey on ficial neural networks as a tool for plant identification:a transfer learning research[J].Journal of software,2015, case study on vietnamese tea accessions[J].Euphytica, 26(1:26-39. 2009,166(3):411-421. 作者简介： [15]陈怡群，常春，肖宏儒，等.人工神经网络技术在鲜茶叶 林丽惠，副教授，主要研究方向为 分选中的应用.农业网络信息，2010(7)：37-40,43. 图像处理和机器学习。主持或参与福 CHEN Yiqun,CHANG Chun,XIAO Hongru,et al.Arti- 建自然科学基金项目多项。表学术论 ficial neural networks technology in the fresh tea 文10余篇。 sorting[J].Agriculture network information,2010(7): 37-40.43. [16]刘自强.鲜茶叶图像特征提取及在茶树品种识别中的 应用研究D].长沙：湖南农业大学，2014. 罗志明.博士研究生，主要研究方 LIU Zigiang.Features extraction of fresh tea images and 向为图像分割、目标检测、医学图像分 析。发表学术论文20余篇。 its application on the recognition of tea varieties[D]. Changsha:Hunan Agricultural University,2014. [17]KRIZHEVSKY A.SUTSKEVER I.HINTON G E.Im- ageNet classification with deep convolutional neural net- works[Cl//Proceedings of the 25th International Confer- 李绍滋，教授，博士生导师，主要 ence on Neural Information Processing Systems.Red 研究方向为计算机视觉、机器学习。 Hook,USA,2012:1097-1105. 主持或参与国家863项目、国家自然 [18]SIMONYAN K,ZISSERMAN A.Very deep convolu- 科学基金项目多项。发表学术论文 tional networks for large-scale image recognition[J].arX- 300余篇。 iv preprint ar Xiv:1409.1556,2014. [19]SZEGEDY C,LIU Wei,JIA Yangqing,et al.Going deep-ing extracts and learns leaf features for plant classifica￾tion[J]. Pattern recognition, 2017, 71: 1–13. GRINBLAT G L, UZAL L C, LARESE M G, et al. Deep learning for plant identification using vein morphological patterns[J]. Computers and electronics in agriculture, 2016, 127: 418–424. [11] PAWARA P, OKAFOR E, SURINTA O, et al. Compar￾ing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition[C]// 6th International Conference on Pattern recognition Ap￾plications and Methods. Porto, Portugal, 2017: 479−486. [12] LIN Lihui, LI C, YANG Sheng, et al. Automated classi￾fication of Wuyi rock tealeaves based on support vector machine[J]. Concurrency and computation: practice and experience, 2019, 31(23): e4519. [13] PANDOLFI C, MUGNAI S, AZZARELLO E, et al. Arti￾ficial neural networks as a tool for plant identification: a case study on vietnamese tea accessions[J]. Euphytica, 2009, 166(3): 411–421. [14] 陈怡群, 常春, 肖宏儒, 等. 人工神经网络技术在鲜茶叶 分选中的应用 [J]. 农业网络信息, 2010(7): 37–40, 43. CHEN Yiqun, CHANG Chun, XIAO Hongru, et al. Arti￾ficial neural networks technology in the fresh tea sorting[J]. Agriculture network information, 2010(7): 37–40, 43. [15] 刘自强. 鲜茶叶图像特征提取及在茶树品种识别中的 应用研究 [D]. 长沙: 湖南农业大学, 2014. LIU Ziqiang. Features extraction of fresh tea images and its application on the recognition of tea varieties[D]. Changsha: Hunan Agricultural University, 2014. [16] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Im￾ageNet classification with deep convolutional neural net￾works[C]//Proceedings of the 25th International Confer￾ence on Neural Information Processing Systems. Red Hook, USA, 2012: 1097−1105. [17] SIMONYAN K, ZISSERMAN A. Very deep convolu￾tional networks for large-scale image recognition[J]. arX￾iv preprint arXiv: 1409.1556, 2014. [18] [19] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deep￾er with convolutions[C]//Proceedings of 2015 IEEE Con￾ference on Computer Vision and Pattern Recognition. Bo￾ston, USA, 2015: 1−9. HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recog￾nition. Las Vegas, USA, 2016: 770−778. [20] 胡越, 罗东阳, 花奎, 等. 关于深度学习的综述与讨 论 [J]. 智能系统学报, 2019, 14(1): 1–19. HU Yue, LUO Dongyang, HUA Kui, et al. Overview on deep learning[J]. CAAI transactions on intelligent sys￾tems, 2019, 14(1): 1–19. [21] 庄福振, 罗平, 何清, 等. 迁移学习研究进展 [J]. 软件学 报, 2015, 26(1): 26–39. ZHUANG Fuzhen, LUO Ping, HE Qing, et al. Survey on transfer learning research[J]. Journal of software, 2015, 26(1): 26–39. [22] 作者简介： 林丽惠，副教授，主要研究方向为 图像处理和机器学习。主持或参与福 建自然科学基金项目多项。表学术论 文 10 余篇。 罗志明，博士研究生，主要研究方 向为图像分割、目标检测、医学图像分 析。发表学术论文 20 余篇。 李绍滋，教授，博士生导师，主要 研究方向为计算机视觉、机器学习。 主持或参与国家 863 项目、国家自然 科学基金项目多项。发表学术论文 300 余篇。 ·924· 智 能 系 统 学 报 第 15 卷