Three-channel convolutional neural networks for vegetable leaf disease recognition

Abstract The color information of diseased leaf is the main basis for leaf based plant disease recognition. To make use of color information, a novel three-channel convolutional neural networks (TCCNN) model is constructed by combining three color components for vegetable leaf disease recognition. In the model, each channel of TCCNN is fed by one of three color components of RGB diseased leaf image, the convolutional feature in each CNN is learned and transmitted to the next convolutional layer and pooling layer in turn, then the features are fused through a fully connected fusion layer to get a deep-level disease recognition feature vector. Finally, a softmax layer makes use of the feature vector to classify the input images into the predefined classes. The proposed method can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases. The experimental results validate that the proposed method outperforms the state-of-the-art methods of the vegetable leaf disease recognition.

[1]  Shanwen Zhang,et al.  Plant Species Recognition Based on Deep Convolutional Neural Networks , 2017, ICIC.

[2]  De-Shuang Huang,et al.  Comparative Study between Radial Basis Probabilistic Neural Networks and Radial Basis Function Neural Networks , 2003, IDEAL.

[3]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Darko Stefanovic,et al.  Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification , 2016, Comput. Intell. Neurosci..

[5]  Trevor Hastie,et al.  Regularized linear discriminant analysis and its application in microarrays. , 2007, Biostatistics.

[6]  Wen-zhun Huang,et al.  A Novel Face Recognition Algorithm based on the Deep Convolution Neural Network and Key Points Detection Jointed Local Binary Pattern Methodology , 2017 .

[7]  De-Shuang Huang,et al.  Locally linear discriminant embedding: An efficient method for face recognition , 2008, Pattern Recognit..

[8]  Qiuyu Xia,et al.  Research on Cucumber Downy Mildew Detection System based on SVM Classification Algorithm , 2015, ICME 2015.

[9]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[10]  Xinmei Tian,et al.  Multi-organ plant identification with multi-column deep convolutional neural networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[11]  Henrik Skov Midtiby,et al.  Plant species classification using deep convolutional neural network , 2016 .

[12]  Biao Leng,et al.  Data augmentation for unbalanced face recognition training sets , 2017, Neurocomputing.

[13]  De-Shuang Huang,et al.  A Neural Root Finder of Polynomials Based on Root Moments , 2004, Neural Computation.

[14]  Xiaofeng Wang,et al.  An efficient local Chan-Vese model for image segmentation , 2010, Pattern Recognit..

[15]  De-Shuang Huang,et al.  A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Alsayed Algergawy,et al.  A Deep Learning-based Approach for Banana Leaf Diseases Classification , 2017, BTW.

[17]  De-Shuang Huang,et al.  Human face recognition based on multi-features using neural networks committee , 2004, Pattern Recognit. Lett..

[18]  De-Shuang Huang,et al.  Cancer classification using Rotation Forest , 2008, Comput. Biol. Medicine.

[19]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[21]  Zhu-Hong You,et al.  Leaf image based cucumber disease recognition using sparse representation classification , 2017, Comput. Electron. Agric..

[22]  De-Shuang Huang,et al.  Pupylation sites prediction with ensemble classification model , 2017, Int. J. Data Min. Bioinform..

[23]  Li Shang,et al.  Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network , 2006, Neurocomputing.

[24]  Zhen Wang,et al.  Cucumber disease recognition based on Global-Local Singular value decomposition , 2016, Neurocomputing.

[25]  Thomas Wiatowski,et al.  A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction , 2015, IEEE Transactions on Information Theory.

[26]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

[27]  Patrick O. Glauner.  Deep Convolutional Neural Networks for Smile Recognition , 2015, ArXiv.

[28]  Wang Xiangdong,et al.  Recognition of Greenhouse Cucumber Disease Based on Image Processing Technology , 2013 .

[29]  Jayme Garcia Arnal Barbedo,et al.  A review on the main challenges in automatic plant disease identification based on visible range images , 2016 .

[30]  Gerald Penn,et al.  Convolutional Neural Networks for Speech Recognition , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[31]  Wenzheng Bao,et al.  Classification of Protein Structure Classes on Flexible Neutral Tree. , 2016, IEEE/ACM transactions on computational biology and bioinformatics.

[32]  De-Shuang Huang,et al.  A constructive approach for finding arbitrary roots of polynomials by neural networks , 2004, IEEE Transactions on Neural Networks.

[33]  Wenzheng Bao,et al.  Prediction of protein structure classes with flexible neural tree. , 2014, Bio-medical materials and engineering.

[34]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[35]  Xiaofeng Wang,et al.  A Novel Density-Based Clustering Framework by Using Level Set Method , 2009, IEEE Transactions on Knowledge and Data Engineering.

[36]  De-Shuang Huang,et al.  Zeroing polynomials using modified constrained neural network approach , 2005, IEEE Transactions on Neural Networks.