[2] Wu L Y, Jin S, Wu M Y et al. Advances in tea leafhopper integrated pest management[J]. Guizhou Agricultural Sciences, 42, 64-68(2014).

[3] Wang Q S, Wang D F, Wu G Y. Research advances on Empoasca vitis (Gothe) in tea trees in China[J]. Fujian Journal of Agricultural Sciences, 28, 615-623(2013).

[4] Yao Y J, Wang J L, He L et al. Research on trap of Empoasca vitis Gothe by yellow board[J]. Journal of Tea, 36, 90-92(2010).

[5] Lu Z Y, Liu S C. Drought identification based on multi feature fusion for early maize image[J]. Laser & Optoelectronics Progress, 54, 081005(2017).

[6] Xu L F, Xu X B, Hu M et al. Corn leaf disease identification based on multiple classifiers fusion[J]. Transactions of the Chinese Society of Agricultural Engineering, 31, 194-201(2015).

[7] Ni J, Yao X, Tian Y C et al. Design and experiments of portable apparatus for plant growth monitoring and diagnosis[J]. Transactions of the Chinese Society of Agricultural Engineering, 29, 150-156(2013).

[8] Cao L P. The research progress on machine recognition of plant diseases and insect pests[J]. Chinese Agricultural Science Bulletin, 31, 244-249(2015).

[9] Ling C D, Chen H, Yang X et al. Fundus image hard exudates detection based on SLIC superpixels and DBSCAN clustering[J]. Journal of Huaqiao University, 36, 399-405(2015).

[10] Sun Z, Song Q, Zhu X et al. A novel ensemble method for classifying imbalanced data[J]. Pattern Recognition, 48, 1623-1637(2015). http://dl.acm.org/citation.cfm?id=2739220

[11] Zhang Y, Li X, Gao X et al. A simple algorithm of superpixel segmentation with boundary constraint[J]. IEEE Transactions on Circuits and Systems for Video Technology, 27, 1502-1514(2017). http://ieeexplore.ieee.org/document/7428902/

[12] Mou L, Zhang X W, Zhang Z et al. Saliency detection optimization method in natural scene[J]. Laser & Optoelectronics Progress, 53, 121501(2016).

[13] Wang X. VCells: Simple and efficient superpixels using edge-weighted centroidal voronoi tessellations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1241-1247(2012).

[14] Zhou J, Zhu J R, Wang M J. Pear orchard scene segmentation based on conditional random fields[J]. Transactions of the Chinese Society for Agricultural Machinery, 46, 8-13(2015).

[15] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel method[J]. IEEE Transation on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012). http://dl.acm.org/citation.cfm?id=2377556

[16] Zhou S G, Zhou A Y, Jin W et al. A fast DBSCAN algorithm[J]. Journal of Software, 11, 735-744(2000).

[17] Li G L, Ma Z H, Huang C et al. Segmentation of color images of grape diseases using K_means clustering algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 26, 32-37(2010).

[18] Vapnik V. Statistical learning theory[M]. New York: Wiley(1998).

[19] Khanmohammadi M, Karami F, Mir-Marqués A et al. Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines[J]. Food Engineering, 142, 17-22(2014). http://www.sciencedirect.com/science/article/pii/S0260877414002441

[20] Sun T, Wu H F, Liang Z G et al. Application of SMOTE arithmetic for unbalaced data[J]. Beijing Biomedical Engineering, 31, 528-530(2012).

[21] Zheng W C, Chen S Y, Wang X Q. Imbalanced datasets based SMOTE-SVM-AID algorithm[J]. Journal of Wuhan University of Technology, 34, 58-62(2012).

[22] Yi B H, Zhu J J, Li J. Imbalanced data classification on micro-credit company customer credit risk assessment using improved SMOTE support vector machine[J]. Chinese Journal of Management Science, 24, 24-30(2016).

[23] Zhang X D. Initial center selection method for k-medoids clustering based on KS sampling[J]. Journal of Inner Mongolia University of Technology, 35, 297-302(2016).