High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank

A fundamental problem that confronts deep neural networks is the requirement of a large amount of data for a system to be efficient in complex applications. Promising results of this problem are made possible through the use of techniques such as data augmentation or transfer learning of pre-trained models in large datasets. But the problem still persists when the application provides limited or unbalanced data. In addition, the number of false positives resulting from training a deep model significantly cause a negative impact on the performance of the system. This study aims to address the problem of false positives and class unbalance by implementing a Refinement Filter Bank framework for Tomato Plant Diseases and Pests Recognition. The system consists of three main units: First, a Primary Diagnosis Unit (Bounding Box Generator) generates the bounding boxes that contain the location of the infected area and class. The promising boxes belonging to each class are then used as input to a Secondary Diagnosis Unit (CNN Filter Bank) for verification. In this second unit, misclassified samples are filtered through the training of independent CNN classifiers for each class. The result of the CNN Filter Bank is a decision of whether a target belongs to the category as it was detected (True) or not (False) otherwise. Finally, an integration unit combines the information from the primary and secondary units while keeping the True Positive samples and eliminating the False Positives that were misclassified in the first unit. By this implementation, the proposed approach is able to obtain a recognition rate of approximately 96%, which represents an improvement of 13% compared to our previous work in the complex task of tomato diseases and pest recognition. Furthermore, our system is able to deal with the false positives generated by the bounding box generator, and class unbalances that appear especially on datasets with limited data.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  M. Donatelli,et al.  Modelling the impacts of pests and diseases on agricultural systems , 2017, Agricultural systems.

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

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Hitoshi Iyatomi,et al.  Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks , 2015, ISVC.

[6]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[7]  Andreas Holzinger,et al.  Augmentor: An Image Augmentation Library for Machine Learning , 2017, J. Open Source Softw..

[8]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .

[9]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[10]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Fei-Fei Li,et al.  Object-Centric Spatial Pooling for Image Classification , 2012, ECCV.

[12]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[13]  Dong Sun Park,et al.  Spectral Analysis of CNN for Tomato Disease Identification , 2017, ICAISC.

[14]  Yun Zhang,et al.  Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks , 2017, Symmetry.

[15]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Shuicheng Yan,et al.  Dual Path Networks , 2017, NIPS.

[17]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Bin Yang,et al.  CRAFT Objects from Images , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Abhinav Gupta,et al.  Training Region-Based Object Detectors with Online Hard Example Mining , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  C. Hollier,et al.  Crop losses due to diseases and their implications for global food production losses and food security , 2012, Food Security.

[23]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Sang Cheol Kim,et al.  A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition , 2017, Sensors.

[25]  P. P. Anderson.  The Future World Food Situation and the Role of Plant Diseases , 2001 .

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

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

[28]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[29]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[30]  Konstantinos P. Ferentinos,et al.  Deep learning models for plant disease detection and diagnosis , 2018, Comput. Electron. Agric..

[31]  Twan van Laarhoven,et al.  L2 Regularization versus Batch and Weight Normalization , 2017, ArXiv.

[32]  Artzai Picón,et al.  Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case , 2017, Comput. Electron. Agric..

[33]  Ramakant Nevatia,et al.  ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Dumitru Erhan,et al.  Deep Neural Networks for Object Detection , 2013, NIPS.

[35]  Per Pinstrup-Andersen,et al.  The future world food situation and the role of plant diseases , 2000 .

[36]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[37]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Anne-Katrin Mahlein.  Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. , 2016, Plant disease.

[39]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[41]  Geoffrey E. Hinton,et al.  Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.