基于高阶残差和参数共享反馈卷积神经网络的农作物病害识别
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摘要
当前,大部分农作物病害图像识别方法主要关注于精度而忽略了鲁棒性.在面向实际环境时,由于噪声干扰和环境因素影响导致识别精度不高.为此提出了一种高阶残差和参数共享反馈的卷积神经网络模型以应用于实际环境农作物病害识别.其中,高阶残差子网络为病害表观提供丰富细致的特征表达,以提高模型识别精度;参数共享反馈子网络用来进一步抑制原深层特征中的背景噪声,以提高模型的鲁棒性.实验结果表明,当面向实际环境农作物病害识别时,本文方法在识别精度和鲁棒性上均优于其他方法.
Most of current crop-disease recognition approaches mainly focus on improving the recognition accuracy on public datasets,while ignoring the recognition robustness.When dealing with real-world recognition problem,the actual recognition accuracy of those approach are often unsatisfactory because of noise interference and environmental influence.To address these issues,we propose a high-order residual and parameter-sharing feedback convolutional neural network(HORPSF) for crop-disease recognition.The high-order residual subnetwork is helpful for improving the recognition accuracy of crop disease.The parameter-sharing feedback subnetwork can effectively depress the background noises and enhance the robustness of the model.Extensive experiment results demonstrate that the proposed HORPSF approach significantly outperforms other competing methods in terms of recognition accuracy and robustness,especially demonstrating superior performance when dealing with the real-world examples of crop-disease recognition.
Most of current crop-disease recognition approaches mainly focus on improving the recognition accuracy on public datasets,while ignoring the recognition robustness.When dealing with real-world recognition problem,the actual recognition accuracy of those approach are often unsatisfactory because of noise interference and environmental influence.To address these issues,we propose a high-order residual and parameter-sharing feedback convolutional neural network(HORPSF) for crop-disease recognition.The high-order residual subnetwork is helpful for improving the recognition accuracy of crop disease.The parameter-sharing feedback subnetwork can effectively depress the background noises and enhance the robustness of the model.Extensive experiment results demonstrate that the proposed HORPSF approach significantly outperforms other competing methods in terms of recognition accuracy and robustness,especially demonstrating superior performance when dealing with the real-world examples of crop-disease recognition.
引文
[1] 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.
[2] Omrani E,Khoshnevisan B,Shamshirband S,et al.Potential of radial basis function-based support vector regression for apple disease detection[J].Measurement,2014,55(1):512-519.
[3] 王志彬,王开义,王书锋,等.基于动态集成的黄瓜叶部病害识别方法[J].农业机械学报,2017,48(9):46-52.Wang Zhibin,Wang Kaiyi,Wang Shufeng,et al.Recognition method of cucumber leaf diseases with dynamic ensemble learning[J].Chinese Society for Agricultural Machinery,2017,48(9):46-52.(in Chinese)
[4] 马浚诚,温皓杰,李鑫星,等.基于图像处理的温室黄瓜霜霉病诊断系统[J].农业机械学报,2017,48(2):195-202.Ma Juncheng,Wen Haojie,Li Xinxing,et al.Downy mildew diagnosis system for greenhouse cucumbers based on image processing[J].Chinese Society for Agricultural Machinery,2017,48(2):195-202.(in Chinese)
[5] 詹曙,王俊,杨福猛,等.基于Gabor特征和字典学习的高斯混合稀疏表示图像识别[J].电子学报,2015,43(03):523-528.Zhan Shu,Wang Jun,Yang Fumeng,et al.Gaussian mixture sparse representation for image recognition based on gabor features and dictionary learning[J].Acta Electronica Sinica,2015,43(03):523-528.(in Chinese)
[6] Mohanty S P Mohanty S P,Hughes D P,Salathe M,et al.Using deep learning for image-based plant disease detection[J].Frontiers in Plant Science,2016,7(1):1-7.
[7] Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Boston:IEEE,2015.1-9.
[8] Krizhevsky A,Sutskever I,Hinton G E,et al.ImageNet classification with deep convolutional neural networks[A].NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems [C].Lake Tahoe,Nevada:IEEE,2012.1097-1105.
[9] Nachtigall L G,Araujo R M,Nachtigall G R,et al.Classification of apple tree disorders using convolutional neural networks[A].28th IEEE International Conference on Tools with Artificial Intelligence[C].San Jose,CA,USA:IEEE,2016.472-476.
[10] Fuentes A,Yoon S,Kim S C,et al.A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition[J].Sensors,2017,17(9):1-21.
[11] Ren S,He K,Girshick R B,et al.Faster R-CNN:towards real-time object detection with region proposal networks[A].Neural Information Processing Systems[C].Montréal,Canada:IEEE,2015.91-99.
[12] Dai J,Li Y,He K,et al.R-FCN:object detection via region-based fully convolutional networks[A].NIPS’16 Proceedings of the 29th International Conference on Neural Information Processing Systems[C].Barcelona:IEEE,2016.379-387.
[13] Liu W,Anguelov D,Erhan D,et al.SSD:single shot multibox detector[A].European Conference on Computer Vision[C].Amsterdam:Springer,2016.21-37.
[14] Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[A].International Conference on Learning Representations[C].San Diego:IEEE,2015.1-14.
[15] He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Las Vegas,Nevada:IEEE,2016.770-778.
[16] 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].Transactions of the Chinese Society of Agricultural Engineering,2017,33(19):209-215.(in Chinese)
[17] 张航,程清,武英洁,等.一种基于卷积神经网络的小麦病害识别方法[J].山东农业科学,2018,50(3):137-141.Zhang Hang,Cheng Qing,Wu Yingjie,et al.A method of wheat disease identification based on convolutional neural network[J].Shandong Agricultural Sciences,2018,50(3):137-141.(in Chinese)
[18] 柯圣财,赵永威,李弼程,等.基于卷积神经网络和监督核哈希的图像检索方法[J].电子学报,2017,45(01):157-163.Ke Shengcai,Zhao Yongwei,Li Bicheng,et al.Image retrieval based on convolutional neural network and kernel-based supervised hashing[J].Acta Electronica Sinica,2017,45(01):157-163.(in Chinese)
[19] 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,2017,71(1):1-13.
[20] Kumar N,Belhumeur P N,Biswas A,et al.Leafsnap:a computer vision system for automatic plant species identification[A].European Conference on Computer Vision[C].Firenze,Italy:Springer,2012.502-516.
[21] Hall D,Mccool C,Dayoub F,et al.Evaluation of features for leaf classification in challenging conditions[A].IEEE Workshop on Applications of Computer Vision[C].Waikoloa Beach,Hawaii:IEEE,2015.797-804.
[22] Yang J,Yu K,Gong Y,et al.Linear spatial pyramid matching using sparse coding for image classification[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Miami,Florida:IEEE,2009.1794-1801.
[23] Chang C,Lin C.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[24] Douze M,Jegou H,Sandhawalia H,et al.Evaluation of GIST descriptors for web-scale image search[A].Proceeding of the ACM International Conference on Image and Video Retrieval[C].Santorini Island,Greece:ACM,2009.1-19.
[2] Omrani E,Khoshnevisan B,Shamshirband S,et al.Potential of radial basis function-based support vector regression for apple disease detection[J].Measurement,2014,55(1):512-519.
[3] 王志彬,王开义,王书锋,等.基于动态集成的黄瓜叶部病害识别方法[J].农业机械学报,2017,48(9):46-52.Wang Zhibin,Wang Kaiyi,Wang Shufeng,et al.Recognition method of cucumber leaf diseases with dynamic ensemble learning[J].Chinese Society for Agricultural Machinery,2017,48(9):46-52.(in Chinese)
[4] 马浚诚,温皓杰,李鑫星,等.基于图像处理的温室黄瓜霜霉病诊断系统[J].农业机械学报,2017,48(2):195-202.Ma Juncheng,Wen Haojie,Li Xinxing,et al.Downy mildew diagnosis system for greenhouse cucumbers based on image processing[J].Chinese Society for Agricultural Machinery,2017,48(2):195-202.(in Chinese)
[5] 詹曙,王俊,杨福猛,等.基于Gabor特征和字典学习的高斯混合稀疏表示图像识别[J].电子学报,2015,43(03):523-528.Zhan Shu,Wang Jun,Yang Fumeng,et al.Gaussian mixture sparse representation for image recognition based on gabor features and dictionary learning[J].Acta Electronica Sinica,2015,43(03):523-528.(in Chinese)
[6] Mohanty S P Mohanty S P,Hughes D P,Salathe M,et al.Using deep learning for image-based plant disease detection[J].Frontiers in Plant Science,2016,7(1):1-7.
[7] Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Boston:IEEE,2015.1-9.
[8] Krizhevsky A,Sutskever I,Hinton G E,et al.ImageNet classification with deep convolutional neural networks[A].NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems [C].Lake Tahoe,Nevada:IEEE,2012.1097-1105.
[9] Nachtigall L G,Araujo R M,Nachtigall G R,et al.Classification of apple tree disorders using convolutional neural networks[A].28th IEEE International Conference on Tools with Artificial Intelligence[C].San Jose,CA,USA:IEEE,2016.472-476.
[10] Fuentes A,Yoon S,Kim S C,et al.A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition[J].Sensors,2017,17(9):1-21.
[11] Ren S,He K,Girshick R B,et al.Faster R-CNN:towards real-time object detection with region proposal networks[A].Neural Information Processing Systems[C].Montréal,Canada:IEEE,2015.91-99.
[12] Dai J,Li Y,He K,et al.R-FCN:object detection via region-based fully convolutional networks[A].NIPS’16 Proceedings of the 29th International Conference on Neural Information Processing Systems[C].Barcelona:IEEE,2016.379-387.
[13] Liu W,Anguelov D,Erhan D,et al.SSD:single shot multibox detector[A].European Conference on Computer Vision[C].Amsterdam:Springer,2016.21-37.
[14] Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[A].International Conference on Learning Representations[C].San Diego:IEEE,2015.1-14.
[15] He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Las Vegas,Nevada:IEEE,2016.770-778.
[16] 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].Transactions of the Chinese Society of Agricultural Engineering,2017,33(19):209-215.(in Chinese)
[17] 张航,程清,武英洁,等.一种基于卷积神经网络的小麦病害识别方法[J].山东农业科学,2018,50(3):137-141.Zhang Hang,Cheng Qing,Wu Yingjie,et al.A method of wheat disease identification based on convolutional neural network[J].Shandong Agricultural Sciences,2018,50(3):137-141.(in Chinese)
[18] 柯圣财,赵永威,李弼程,等.基于卷积神经网络和监督核哈希的图像检索方法[J].电子学报,2017,45(01):157-163.Ke Shengcai,Zhao Yongwei,Li Bicheng,et al.Image retrieval based on convolutional neural network and kernel-based supervised hashing[J].Acta Electronica Sinica,2017,45(01):157-163.(in Chinese)
[19] 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,2017,71(1):1-13.
[20] Kumar N,Belhumeur P N,Biswas A,et al.Leafsnap:a computer vision system for automatic plant species identification[A].European Conference on Computer Vision[C].Firenze,Italy:Springer,2012.502-516.
[21] Hall D,Mccool C,Dayoub F,et al.Evaluation of features for leaf classification in challenging conditions[A].IEEE Workshop on Applications of Computer Vision[C].Waikoloa Beach,Hawaii:IEEE,2015.797-804.
[22] Yang J,Yu K,Gong Y,et al.Linear spatial pyramid matching using sparse coding for image classification[A].IEEE Conference on Computer Vision and Pattern Recognition[C].Miami,Florida:IEEE,2009.1794-1801.
[23] Chang C,Lin C.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[24] Douze M,Jegou H,Sandhawalia H,et al.Evaluation of GIST descriptors for web-scale image search[A].Proceeding of the ACM International Conference on Image and Video Retrieval[C].Santorini Island,Greece:ACM,2009.1-19.