[1] SHAMSHIRBAND S, ANUAR N B, KIAH M L M, et al. Survey an appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique [J]. Eng Appl Artif Intell, 2013, 26(9): 2105 − 2127.
[2] HILLNHUTTER C, MAHLEIN A. Early detection and localisation of sugar beet diseases: new approaches [J]. Gesunde Pflanzen, 2008, 60(4): 143 − 149.
[3] CAMARGO A, SMITH J S. An image-processing based algorithm to automatically identify plant disease visual symptoms [J]. Biosyst Eng, 2009, 102(1): 9 − 21.
[4] MUTKA A M, BART R S. Image-based phenotyping of plant disease symptoms [J]. Front Plant Sci, 2015, 5: 1 − 8.
[5] HIARY H A, AHMAD S B, REYALAT M, et al. Fast and accurate setection and classification of plant diseases [J]. Int J Comput Appl, 2011, 17(1): 31 − 38.
[6] TIAN Yuan, ZHAO Chunjiang, LU Shenglian, et al. Multiple classifier combination for recognition of wheat leaf diseases [J]. Intell Automation Soft Comput, 2011, 17(5): 519 − 529.
[7] 秦丰, 刘东霞, 孙炳达, 等. 基于图像处理技术的4种苜蓿叶部病害的识别[J]. 中国农业大学学报, 2016, 21(10): 65 − 75.

QIN Feng, LIU Dongxia, SUN Bingda, et al. Recognition of four different alfalfa leaf diseases based on image processing technology [J]. J China Agric Univ, 2016, 21(10): 65 − 75.
[8]

BARBEDO J G A. An automatic method to detect and measure leaf disease symptoms using digital Image processing [J]. Plant Dis, 2014, 98(12): 1709 − 1716.
[9]

BARBEDO J G A. A new automatic method for disease symptom segmentation in digital photographs of plant leaves [J]. Eur J Plant Pathol, 2016, 147(2): 349 − 364.
[10]

HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition [C]// BAJCSY R, LI Feifei, TUYTELAARS T. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE Press, 2015: 770−778.
[11]

MOHANTY S P, HUGHES D P, SALATHÉ M. Using deep learning for image-based plant disease detection [J]. Front Plant Sci, 2016, 7: 1419.
[12]

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. Commun ACM, 2017, 60(6): 84 − 90.
[13]

SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions [C]// BISCHOF H, FORSYTH D, SCHMID C, et al. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE Press, 2015: 1 − 9.
[14] 孙俊, 谭文军, 毛罕平, 等. 基于改进卷积神经网络的多种植物叶片病害识别[J]. 农业工程学报, 2017, 33(19): 209 − 215.

SUN Jun, TAN Wenjun, MAO Hanping, et al. Recognition of multiple plant leaf diseases based on improved convolutional neural network [J]. Trans Chin Soc Agric Eng, 2017, 33(19): 209 − 215.
[15] 龙满生, 欧阳春娟, 刘欢, 等. 基于卷积神经网络与迁移学习的油茶病害图像识别[J]. 农业工程学报, 2018, 34(18): 194 − 201.

LONG Mansheng, OUYANG Chunjuan, LIU Huan, et al. Image recognition of Camellia oleifera diseases based on convolutional neural network & transfer learning [J]. Trans Chin Soc Agric Eng, 2018, 34(18): 194 − 201.
[16] 张建华, 孔繁涛, 吴建寨, 等. 基于改进VGG卷积神经网络的棉花病害识别模型[J]. 中国农业大学学报, 2018, 23(11): 161 − 171.

ZHANG Jianhua, KONG Fantao, WU Jianzhai, et al. Cotton disease identification model based on improved VGG convolution neural network [J]. J China Agric Univ, 2018, 23(11): 161 − 171.
[17]

DECHANT C, WIESNER-HANKS T, CHEN Siyuan, et al. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning [J]. Phytopathology, 2017, 107(11): 1426 − 1432.
[18]

PICON A, ALVAREZ-GILA A, SEITZ M, et al. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild [J]. Comput Electron Agric, 2019, 161: 280 − 290.
[19]

WANG Y P E, LIN Xingqin, ADHIKARY A, et al. A primer on 3GPP narrowband internet of things [J]. IEEE Commun Mag, 2017, 55(3): 117 − 123.
[20] 陈方. MobileNet压缩模型的研究与优化[D]. 武汉: 华中师范大学, 2018.

CHEN Fang. Research and Optimization of MobileNet Compression Model [D]. Wuhan: Central China Normal University, 2018.
[21]

HAN S, POOL J, TRAN J, et al. Learning both weights and connections for efficient neural networks [C]// CORTES C, LAWRENCE N D, LEE D D, et al. The 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2015: 1135 − 1143.
[22]

JACOB B, KLIGYS S, CHEN B, et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference [C]// BROWN M, MORSE B, PELEG S, et al. 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE Press, 2018: 2704 − 2713.
[23]

BA J L, CARUANA R. Do deep nets really need to be deep [C]// GHAHRAMANI Z, WELLING M, CORTES C, et al. The 27th International Conference on Neural Information Processing Systems [C]. Montreal: MIT Press, 2014: 2654 − 2662.