Lightweight compressed depth neural network for tomato disease diagnosis

Aiming at the shortcoming of deep neural network in crop disease diagnosis, a lightweight compressed depth neural network for tomato disease diagnosis is proposed. Multi-scale convolution is used to increase receptive field, extract more abundant features, reduce model parameters and realize lightweight of the network by adopting the strategies of group convolution, depth separable convolution, pointwise convolution, channel shuffle, etc. For the lightweight model that has been initially trained, pruning operation is used to cut filter weight that is not important to reduce redundancy of the model. Experiments show that the accuracy of tomato disease diagnosis using the lightweight model is 98.61% after training only 10 epochs, it meets the needs of tomato disease diagnosis in agricultural production due to the small calculation and fast detection speed, and when cutting about 50% filter weight, the accuracy has only dropped 0.70%, which has a good effect.

[1]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Abdelouahab Moussaoui,et al.  Deep Learning for Tomato Diseases: Classification and Symptoms Visualization , 2017, Appl. Artif. Intell..

[3]  Sepp Hochreiter,et al.  Speeding up Semantic Segmentation for Autonomous Driving , 2016 .

[4]  Wei Sun,et al.  PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network , 2019, Comput. Electron. Agric..

[5]  S. Zhang,et al.  Plant disease recognition based on plant leaf image. , 2015 .

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

[7]  Zhiqiang Shen,et al.  Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[9]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[11]  Ting Liu,et al.  Recent advances in convolutional neural networks , 2015, Pattern Recognit..

[12]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

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

[14]  Xiangyu Zhang,et al.  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Michael Carbin,et al.  The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks , 2018, ICLR.

[16]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .

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

[18]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[19]  Jason Yosinski,et al.  Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask , 2019, NeurIPS.

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