[1] Nilsback M E, Zisserman A. A visual vocabulary for flower classification. [C]∥2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), June 17-22, 2006, New York, NY, USA. New York: IEEE(2006).
[2] Wang L J, Huai Y J, Peng Y C[J]. Method of identification of foliage from plants based on extraction of multiple features of leaf images Journal of Beijing Forestry University, 2015, 55-61.
[3] Wang X F, Huang D S, Du J X et al. Feature extraction and recognition for leaf images[J]. Computer Engineering and Applications, 42, 190-193(2006).
[4] Yang T T, Pan X X, Mu L Q. Identification of seven salix species using digital information analysis of leaf image characteristics[J]. Journal of Northeast Forestry University, 42, 75-79(2014).
[5] Gong D X, Cao C R[J]. Plant leaf classification based on CNN Computer and Modernization, 2014, 12-15, 19.
[6] Liu J J. Research and implementation of plant leaf recognition algorithms based on deep network feature learning[D]. Shenzhen: Shenzhen University, 15-19(2017).
[7] Kadir A, Nugroho L E, Susanto A et al. Neural network application on foliage plant identification[J]. Computer Science, 11, 15-22(2011).
[8] Zhang S, Huai Y J. Leaf image recognition based on layered convolutions neural network deep learning[J]. Journal of Beijing Forestry University, 38, 108-115(2016).
[9] Hu Z F. Research and implementation of plant images recognition methods[D]. Hangzhou: Zhejiang University, 16-21(2017).
[10] Peng H L. Research on Chinese herbal medicine plant image retrieval based on deep learning and hashing learning[D]. Jinan: Shandong University, 36-42(2018).
[11] Yuan Y, Wang D B, Liu Y J[J]. Research on plant image recognition method based on depth learning Modern Agricultural Science and Technology, 2017, 278-280.
[12] Yuan P S, Li W, Ren S G et al. Recognition for flower type and variety of Chrysanthemum with convolutional neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 34, 152-158(2018).
[13] Zhu X L, Zhu M, Ren H. Method of plant leaf recognition based on improved deep convolutional neural network[J]. Cognitive Systems Research, 52, 223-233(2018).
[14] Hu J, Chen Z B, Yang M et al. A multi-scale fusion convolutional neural network for plant leaf recognition[J]. IEEE Signal Processing Letters, 25, 853-857(2018).
[18] Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines. [C]∥Proceedings of the 27th international conference on machine learning (ICML-10), June 21-24, 2010, Haifa, Israel.[S.l.:s.n.], 807-814(2010).
[19] Li Y D, Hao Z B, Lei H. Survey of convolutional neural network[J]. Journal of Computer Applications, 36, 2508-2515, 2565(2016).
[20] Zheng Y L, Zhang L. Plant leaf image recognition method based on transfer learning with convolutional neural networks[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 354-359(2018).
[21] Kaiming H, Georgia G, Piotr D et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-1(2018).
[22] Girshick R. Fast R-CNN. [C]∥2015 IEEE International Conference on Computer Vision(ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 1440-1448(2015).
[24] Visual Geometry Group[EB/OL]. -02-20)[2019-07-07]. http:∥www.robots.ox.ac.uk/~vgg/data/flowers/102/.(2009).
[25] [OL]. Plant photo bank of China http:∥ppbc.iplant.cn/..
[26] Chawla N V, Bowyer K W, Hall L O et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 16, 321-357(2002).
[27] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).
