Performance Analysis of Efficient Pre-trained Networks based on Transfer Learning for Tomato Leaf Diseases Classification

Early diagnosis and accurate identification to tomato leaf diseases contribute on controlling the diffusion of infection and guarantee healthy to the plant which in role result in increasing the crop harvest. Nine common types of tomato leaf diseases have a great effect on the quality and quantity of tomato crop yield. The tradition approaches of features extraction and image classification cannot ensure a high accuracy rate for leaf diseases identification. This paper suggests an automatic detection approach for tomato leaf diseases based on the fine tuning and transfer learning to the pre-trained of deep Convolutional Neural Networks. Three pre-trained deep networks based on transfer learning: AlexNet, VGG-16 Net and SqueezeNet are suggested for their performances analysis in tomato leaf diseases classification. The proposed networks are carried out on two different dataset, one of them is a small dataset using only four different diseases while the other is a large dataset of leaves accompanied with symptoms of nine diseases and healthy leaves. The performance of the suggested networks is evaluated in terms of classification accuracy and the elapsed time during their training. The performance of the suggested networks using the small dataset are also compared with that of the-state-of-the-art technique in literature. The experimental results with the small dataset demonstrate that the accuracy of classification of the suggested networks outperform by 8.1% and 15% over the classification accuracy of the technique in literature. On other side when using the large dataset, the proposed pre-trained AlexNet achieves high classification accuracy by 97.4% and the consuming time during its training is lower than those of the other pre-trained networks. Generally, it can be concluded that AlexNet has outstanding performance for diagnosing the tomato leaf diseases in terms of accuracy and execution time compared to the other networks. On contrary, the performance of VGG-16 Net in metric of classification accuracy is the best yet the largest consuming time among other networks.

[1]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[2]  Yi Zhang,et al.  Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks , 2017, Symmetry.

[3]  Mohamed Batouche,et al.  Investigation on deep learning for off-line handwritten Arabic character recognition , 2017, Cognitive Systems Research.

[4]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

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

[6]  Maryam Rahnemoonfar,et al.  Deep Count: Fruit Counting Based on Deep Simulated Learning , 2017, Sensors.

[7]  Han Zhongzhi,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2019, Computer Vision-Based Agriculture Engineering.

[8]  Suyash Bhardwaj,et al.  Potato Leaf Diseases Detection Using Deep Learning , 2020, 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS).

[9]  Sushama Shelke,et al.  Handwritten Devanagari Character Classification using Deep Learning. , 2018, 2018 International Conference on Information , Communication, Engineering and Technology (ICICET).

[10]  Yang Lu,et al.  Identification of rice diseases using deep convolutional neural networks , 2017, Neurocomputing.

[11]  Naoyuki Kubota,et al.  Joint probabilistic approach for real-time face recognition with transfer learning , 2016, Robotics Auton. Syst..

[12]  Xiaogang Wang,et al.  Hybrid Deep Learning for Face Verification , 2013, 2013 IEEE International Conference on Computer Vision.

[13]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[14]  Xiaogang Wang,et al.  Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

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

[16]  Wu,et al.  CNN intelligent early warning for apple skin lesion image acquired by infrared video sensors , 2016 .

[17]  Cai Cheng,et al.  Weed seeds classification based on PCANet deep learning baseline , 2015, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

[18]  Ali Abd Almisreb,et al.  Utilizing AlexNet Deep Transfer Learning for Ear Recognition , 2018, 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP).

[19]  Peter Peer,et al.  Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[20]  Xi Cheng,et al.  Pest identification via deep residual learning in complex background , 2017, Comput. Electron. Agric..

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

[22]  Mostafa Mehdipour-Ghazi,et al.  Plant identification using deep neural networks via optimization of transfer learning parameters , 2017, Neurocomputing.

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

[24]  Yimin D. Zhang,et al.  Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[25]  Paolo Remagnino,et al.  Deep-plant: Plant identification with convolutional neural networks , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[26]  Marcel Salathé,et al.  An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing , 2015, ArXiv.

[27]  Guanlin Li,et al.  Image Recognition of Grape Downy Mildew and Grape Powdery Mildew Based on Support Vector Machine , 2011, CCTA.

[28]  . Girish Athanikar,et al.  Potato Leaf Diseases Detection and Classification System Mr , 2016 .

[29]  Marcel Salathé,et al.  Inference of Plant Diseases from Leaf Images through Deep Learning , 2016 .

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

[31]  Zhihai Lu,et al.  Pathological brain detection based on AlexNet and transfer learning , 2019, J. Comput. Sci..

[32]  A. J. Hanson,et al.  Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network , 2017 .

[33]  Li Bai,et al.  Deep Learning in Visual Computing and Signal Processing , 2017, Appl. Comput. Intell. Soft Comput..

[34]  Lei Zhang,et al.  Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Feng Jiang,et al.  Plant identification based on very deep convolutional neural networks , 2017, Multimedia Tools and Applications.

[36]  Cyrill Stachniss,et al.  Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming , 2018, IEEE Robotics and Automation Letters.

[37]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

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

[39]  Lei Zhang,et al.  Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network , 2019, Int. J. Agric. Environ. Inf. Syst..

[40]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[41]  Adem Tuncer,et al.  Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm , 2018, 2018 3rd International Conference on Computer Science and Engineering (UBMK).

[42]  Qiufeng Wu,et al.  Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks , 2020 .