An End-To-End Practical Plant Disease Diagnosis System for Wide-Angle Cucumber Images

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    With the breakthrough of deep learning techniques, many leaf-based automated plant diagnosis methodologies have been proposed. To the best of our knowledge, most conventional methodologies only accept narrow range images, typically one or quite a limited number of targets are in their input. This is because the appearance of leaves is diverse and leaves usually heavily overlap each other in practical situations. In this paper, we propose a basic and practical end-to-end plant disease diagnosis system for wide-angle images. Our system is principally composed of two specially designed types of convolutional neural networks. The system achieves leaf detection performance of 73.9% in F1-score, overall (detection and diagnosis) performance of 68.1% in recall and 65.8% in precision at around 3 seconds/image on 500 wide-angle on-site images which have 6,860 healthy and 6,741 infected leaves (13,601 in total).

     

     


  • Keywords


    deep learning; convolutional neural networks; cucumber plant disease; automated disease diagnosis; object detection.

  • References


      [1] Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H. Identification of alfalfa leaf diseases using image recognition technology. Plos One, 11(12), 2016, 1-26.

      [2] Cortes C, Vapnik V. Support-vector networks. Machine Learning, 20(3), 1995, 273-297.

      [3] Hallau L, Neumann M, Klatt B, Kleinhenz B, Klein T, Kuhn C, Röhrig M, Bauckhage C, Kersting K, Mahlein A K, Steiner U. Automated identification of sugar beet diseases using smartphones. Plant Pathology, 67(2), 2018, 399-410.

      [4] Mwebaze E, Owomugisha G. Machine learning for plant disease incidence and severity measurements from leaf images. Proceedings of the 15th IEEE International Conference on on Machine Learning and Applications, 2016, 158-163.

      [5] Rublee E, Rabaud V, Konolige K, Bradski G. ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE international conference on Computer Vision, 2011, 2564-2571.

      [6] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, 2012, 1097-1105.

      [7] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg A C. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 2015, 211-252.

      [8] Liu B, Zhang Y, He D, Li Y. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 2017, 11-26.

      [9] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 1-9.

      [10] Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7(1419), 2016, 1-10.

      [11] Hughes D, Salathé M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. 2015, CoRR, abs/1511.08060.

      [12] Wang G, Sun Y, Wang J. Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience, 2017, 1-8.

      [13] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014, CoRR, abs/1409.1556.

      [14] Durmuş H, Güneş E O, Kırcı M. Disease detection on the leaves of the tomato plants by using deep learning. Proceedings of the IEEE 6th International Conference on Agro-Geoinformatics, 2017, 1-5.

      [15] Iandola F N, Han S, Moskewicz M W, Ashraf K, Dally W J, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. 2016, CoRR, abs/1602.07360.

      [16] Atabay H A. Deep residual learning for tomato plant leaf disease identification. Journal of Theoretical and Applied Information Technology, 95(24), 2017, 6800-6808.

      [17] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Computer Vision and Pattern Recognition, 2016, 770-778.

      [18] Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification. Lecture Notes in Informatics, 2017, 79-88.

      [19] LeCun Y, Boser B, Denker J S, Henderson D, Howard R E, Hubbard W, Jackel L D. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 1989, 541-551.

      [20] Fujita E, Kawasaki Y, Uga H, Kagiwada S, Iyatomi H. Basic investigation on a robust and practical plant diagnostic system. Proceedings of the IEEE Machine Learning and Applications, 2016, 989-992.

      [21] Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 1-11.

      [22] Ferentinos K P. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 2018, 311-318.

      [23] Ramcharan A, Baranowski K, McCloskey P, Ahamed B, Legg J, Hughes D. Deep learning for image-based cassava disease detection. Frontiers in Plant Science, 8, 2017, 1-7.

      [24] Atole R R, Park D. A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies. International Journal of Advanced Computer Science and Applications, 9(1), 2018, 67-70.

      [25] Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 2017, 1137-1149.

      [26] Dai J, Li Y, He K, Sun J. R-FCN: Object detection via region-based fully convolutional networks. Proceedings of the Advances in Neural Information Processing Systems, 2016, 379-387.

      [27] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, Berg A C. SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, 2016, 21-37.

      [28] Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y. Deformable convolutional networks. 2017, CoRR, abs/1703.06211.

      [29] Fuentes A, Yoon S, Kim S C, Park D S. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2017, 1-21.

      [30] Lu J, Hu J, Zhao G, Mei F, Zhang C. An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 142, 2017, 369-379.

      [31] Xia C, Lee J M, Li Y, Song Y H, Chung B K, Chon T S. Plant leaf detection using modified active shape models. Biosystems Engineering, 116(1), 2013, 23-35.

      [32] Cap H Q, Suwa K, Fujita E, Kagiwada S, Uga H, Iyatomi H. A deep learning approach for on-site plant leaf detection. Proceedings of the 14th IEEE International Colloquium on Signal Processing and its Applications, 2018, 120-124.

      [33] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 2015, CoRR, abs/1502.03167.

      [34] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 2014, 1929-1958.

      [35] Kingma D P, Ba J. Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations, 2014, 1-15.

      [36] Qian N. On the momentum term in gradient descent learning algorithms. Neural Networks, 12(1), 1999, 145-151.


 

View

Download

Article ID: 20784
 
DOI: 10.14419/ijet.v7i4.11.20784




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.