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刁智华, 刁春迎, 袁万宾, 等.基于改进型模糊边缘检测的小麦病斑阈值分割算法[J]. 农业工程学报, 2018, 34(10):147-152. DIAO Zhihua, DIAO Chunying, YUAN Wanbin, et al. Segmentation algorithm with threshold for wheat lesion based on improved fuzzy edge detection [J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(10): 147-152.
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秦立峰, 何东健, 宋怀波.词袋特征PCA多子空间自适应融合的黄瓜病害识别[J]. 农业工程学报, 2018, 34(08):200-205. QIN Lifeng, HE Dongjian, SONG Huaibo. Bag of words feature multi-PCA subspace adaptive fusion for cucumber diseases identification[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(8): 200-205.
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魏丽冉, 岳峻, 李振波, 等.基于核函数支持向量机的植物叶部病害多分类检测方法[J]. 农业机械学报, 2017,48(S1):166-171.WEI Liran, YUE Jun, LI Zhenbo, et al. Multi-classification detection method of plant leaf disease based on kernel function SVM[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(S1):166-171.
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肖志云, 刘洪.马铃薯典型病害图像自适应特征融合与快速识别[J]. 农业机械学报, 2017, 48(12):26-32.XIAO Zhiyun LIU Hong. Adaptive features fusion and fastrecognition of potato typical disease images[J]. Transactions of the Chinese Society for Agricultural Machinery. 2017, 48(12):26-32.
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张经纬,贡亮,黄亦翔,等.基于随机森林算法的黄瓜种子腔图像分割方法[J]. 农机化研究, 2017, 39(10):163-168. ZHANG Jingwei, GONG Liang, HUANG Yixiang, et al. Image segmentation of cucumber seed cavity based on the random forest algorithm[J]. Journal of Agricultural Mechanization Research, 2017, 39(10):163-168.
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田凯, 张连宽, 熊美东, 等.基于叶片病斑特征的茄子褐纹病识别方法[J]. 农业工程学报, 2016, 32(S1):184-189. TIAN Kai, ZHANG Liankuan, XIONG Meidong, et al. Recognition of phomopsis vexans in solanum melongena based on leaf disease spot features[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(S1): 184-189.
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许良凤, 徐小兵, 胡敏,等. 基于多分类器融合的玉米叶部病害识别[J]. 农业工程学报, 2015,31(14):194-201.XU Liangfeng, XU Xiaobing, HU Min, 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.
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WANG Y B, YOU Z H, LI X, et al. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network[J]. Molecular Biosystems, 2017, 13(7):1336-1344.
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WANG L, YOU Z, CHEN X, et al. Computational methods for the prediction of drug-target interactions from drug fingerprints and protein sequences by stacked auto-encoder deep neural network[C]. International Symposium on Bioinformatics Research and Applications, 2017: 46-58.
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张善文, 张传雷, 丁军.基于改进深度置信网络的大棚冬枣病虫害预测模型[J]. 农业工程学报, 2017,33(19):202-208. ZHANG Shanwen, ZHANG Chuanlei, DING Jun. Disease and insect pest forecasting model of greenhouse winter jujube based on modified deep belief network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(19): 202-208.
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黄双萍, 孙超, 齐龙,等.基于深度卷积神经网络的水稻穗瘟病检测方法[J]. 农业工程学报, 2017,33(20):169-176.HUANG Shuangping, SUN Chao, QI Long,et al. Rice panicle blast identification method based on deep convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(20): 169-176.
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JEON W, RHEE S. Plant leaf recognition using a convolution neural network[J]. The International Journal of Fuzzy Logic and Intelligent Systems, 2017, 17(1): 26-34.
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SLADOJEVIC S, ARSENOVIC M, ANDERLA A, et al. Deep neural networks based recognition of plant diseases by leaf image classification[J]. Computational Intelligence and Neuroscience, 2016.
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HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[J]. 2017,arXiv Preprint,arXiv: 1709.01507.
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HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[J]. Computer Vision and Pattern Recognition, 2016: 770-778.
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HUGHES D P, SALATHE M. An open access repository of images on plant health to enable the development of mobile disease diagnostics[J]. arXiv: Computers and Society, 2015.
|
[1] |
刁智华, 刁春迎, 袁万宾, 等.基于改进型模糊边缘检测的小麦病斑阈值分割算法[J]. 农业工程学报, 2018, 34(10):147-152. DIAO Zhihua, DIAO Chunying, YUAN Wanbin, et al. Segmentation algorithm with threshold for wheat lesion based on improved fuzzy edge detection [J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(10): 147-152.
|
[2] |
秦立峰, 何东健, 宋怀波.词袋特征PCA多子空间自适应融合的黄瓜病害识别[J]. 农业工程学报, 2018, 34(08):200-205. QIN Lifeng, HE Dongjian, SONG Huaibo. Bag of words feature multi-PCA subspace adaptive fusion for cucumber diseases identification[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(8): 200-205.
|
[3] |
魏丽冉, 岳峻, 李振波, 等.基于核函数支持向量机的植物叶部病害多分类检测方法[J]. 农业机械学报, 2017,48(S1):166-171.WEI Liran, YUE Jun, LI Zhenbo, et al. Multi-classification detection method of plant leaf disease based on kernel function SVM[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(S1):166-171.
|
[4] |
肖志云, 刘洪.马铃薯典型病害图像自适应特征融合与快速识别[J]. 农业机械学报, 2017, 48(12):26-32.XIAO Zhiyun LIU Hong. Adaptive features fusion and fastrecognition of potato typical disease images[J]. Transactions of the Chinese Society for Agricultural Machinery. 2017, 48(12):26-32.
|
[5] |
张经纬,贡亮,黄亦翔,等.基于随机森林算法的黄瓜种子腔图像分割方法[J]. 农机化研究, 2017, 39(10):163-168. ZHANG Jingwei, GONG Liang, HUANG Yixiang, et al. Image segmentation of cucumber seed cavity based on the random forest algorithm[J]. Journal of Agricultural Mechanization Research, 2017, 39(10):163-168.
|
[6] |
田凯, 张连宽, 熊美东, 等.基于叶片病斑特征的茄子褐纹病识别方法[J]. 农业工程学报, 2016, 32(S1):184-189. TIAN Kai, ZHANG Liankuan, XIONG Meidong, et al. Recognition of phomopsis vexans in solanum melongena based on leaf disease spot features[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(S1): 184-189.
|
[7] |
许良凤, 徐小兵, 胡敏,等. 基于多分类器融合的玉米叶部病害识别[J]. 农业工程学报, 2015,31(14):194-201.XU Liangfeng, XU Xiaobing, HU Min, 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.
|
[8] |
ZHANG S, WANG H, HUANG W, et al. Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG[J]. Optik, 2018: 866-872.
|
[9] |
KAMAL M M, MASAZHAR A N, RAHMAN F D, et al. Classification of leaf disease from image processing technique[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2018, 10(1): 191-200.
|
[10] |
WANG Y B, YOU Z H, LI X, et al. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network[J]. Molecular Biosystems, 2017, 13(7):1336-1344.
|
[11] |
WANG L, YOU Z, CHEN X, et al. Computational methods for the prediction of drug-target interactions from drug fingerprints and protein sequences by stacked auto-encoder deep neural network[C]. International Symposium on Bioinformatics Research and Applications, 2017: 46-58.
|
[12] |
张善文, 张传雷, 丁军.基于改进深度置信网络的大棚冬枣病虫害预测模型[J]. 农业工程学报, 2017,33(19):202-208. ZHANG Shanwen, ZHANG Chuanlei, DING Jun. Disease and insect pest forecasting model of greenhouse winter jujube based on modified deep belief network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(19): 202-208.
|
[13] |
黄双萍, 孙超, 齐龙,等.基于深度卷积神经网络的水稻穗瘟病检测方法[J]. 农业工程学报, 2017,33(20):169-176.HUANG Shuangping, SUN Chao, QI Long,et al. Rice panicle blast identification method based on deep convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(20): 169-176.
|
[14] |
SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[J]. Computer Vision and Pattern Recognition, 2015: 1-9.
|
[15] |
NACHTIGALL L G, ARAUJO R M, NACHTIGALL G R, et al. Classification of apple tree disorders using convolutional neural networks[C]. International Conference on Tools with Artificial Intelligence, 2016: 472-476.
|
[16] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E, et al. ImageNet classification with deep convolutional neural networks[C]. Neural Information Processing Systems, 2012: 1097-1105.
|
[17] |
LEE S H, CHAN C S, MAYO S J, et al. How deep learning extracts and learns leaf features for plant classification[J]. Pattern Recognition, 2017: 1-13.
|
[18] |
JEON W, RHEE S. Plant leaf recognition using a convolution neural network[J]. The International Journal of Fuzzy Logic and Intelligent Systems, 2017, 17(1): 26-34.
|
[19] |
SLADOJEVIC S, ARSENOVIC M, ANDERLA A, et al. Deep neural networks based recognition of plant diseases by leaf image classification[J]. Computational Intelligence and Neuroscience, 2016.
|
[20] |
DURMUS H, GUNES E O, KIRCI M, et al. Disease detection on the leaves of the tomato plants by using deep learning[C]. international conference on agro-geoinformatics, 2017: 1-5.
|
[21] |
HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[J]. 2017,arXiv Preprint,arXiv: 1709.01507.
|
[22] |
DENG J, DONG W, SOCHER R, et al.ImageNet: a large-scale hierarchical image database[C]. Computer Vision and Pattern Recognition, 2009: 248-255.
|
[23] |
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[J]. Computer Vision and Pattern Recognition, 2016: 770-778.
|
[24] |
HUGHES D P, SALATHE M. An open access repository of images on plant health to enable the development of mobile disease diagnostics[J]. arXiv: Computers and Society, 2015.
|