ISSN 0253-2778

CN 34-1054/N

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Research on high-order residual convolution neural network for crop disease recognition application

  • 1.

    Institute of Intelligent Machines, Chinese Academy of Sciences. Hefei 230031, China

  • 2.

    School of Information Science, University of Science and Technology of China. Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.10.002
  • Received Date: 30 June 2018
  • Accepted Date: 28 September 2018
  • Rev Recd Date: 28 September 2018
  • Publish Date: 31 October 2019
    Abstract

  • Abstract

    Current research works focusing on the image recognition of crop disease in simple background have achieved great success. However, when handling the problem of crop disease recognition with various noise and complex backgrounds, it is difficult to meet the requirement of recognition accuracy. To address these issues, a new high-order residual convolution neural network for crop disease recognition is proposed, which can realize crop disease recognition that is both accurate and anti-interference. Extensive experimental results demonstrate that the proposed method has high accuracy, strong robustness as well as good anti-interference ability, and can better meet the practical application requirements for crop disease recognition.

    Abstract

    Current research works focusing on the image recognition of crop disease in simple background have achieved great success. However, when handling the problem of crop disease recognition with various noise and complex backgrounds, it is difficult to meet the requirement of recognition accuracy. To address these issues, a new high-order residual convolution neural network for crop disease recognition is proposed, which can realize crop disease recognition that is both accurate and anti-interference. Extensive experimental results demonstrate that the proposed method has high accuracy, strong robustness as well as good anti-interference ability, and can better meet the practical application requirements for crop disease recognition.
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    • References(24)
  • [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.
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    [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.
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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.
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    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.
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    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.
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    [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.
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    [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.
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