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Original Articles

Deep Learning for Tomato Diseases: Classification and Symptoms Visualization

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References

  • Akhtar, A., A. Khanum, S. A. Khan, and A. Shaukat. 2013. Automated Plant Disease Analysis (APDA): Performance comparison of machine learning techniques. Proceedings of the 11th International Conference on Frontiers of Information Technology, 60–65. IEEE Computer Society, Islamabad, Pakistan.
  • Al Hiary, H., S. Bani Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh. 2011. Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications 17:31–38. doi:10.5120/ijca.
  • Alex, K., I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Neural Information Processing Systems (NIPS),ed. F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Curran Associates Inc.: Lake Tahoe, Nevada, USA, 1097–105.
  • Bahrampour, S., N. Ramakrishnan, L. Schott, and M. Shah. 2015. Comparative study of Caffe, Neon, Theano, and Torch for Deep Learning. ArXiv 2:1–14.
  • Blancard, D. 2012. Tomato diseases. The Netherlands: Academic Press.
  • Breitenreiter, A., H. Poppinga, T. U. Berlin, and F. N. Technik. 2015. Deep learning. Nature 521:2015.
  • Dandawate, Y., and R. Kokare. 2015. An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective. Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India, 794–99. IEEE.
  • FAOSTAT. 2016. Food and agriculture organization of the United Nations Database, Food and Agriculture Organization Corporate Statistical Database, Rome, Italy.
  • Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT Press Cambridge, Massachusetts, London, England.
  • Grün, F., C. Rupprecht, N. Navab, and F. Tombari. 2016. A taxonomy and library for visualizing learned features in convolutional neural networks. Proceedings of the Workshop on Visualization for Deep Learning at International Conference on Machine Learning (ICML), New York, USA, 48.
  • Hanssen, I. M., and M. Lapidot. 2012. Major tomato viruses in the mediterranean basin. In Advances in virus research, volume 84 of advances in virus research, ed. G. Loebenstein and H. Lecoq, 31–66. Academic Press: San Diego, California, USA.
  • Koike, S. T., P. Gladders, and A. O. Paulus. 2007. Vegetable diseases: A color handbook. Ed Academic Press: San Diego, California, USA.
  • Le, T.-L., N.-D. Duong, V.-T. Nguyen, and H. Vu. 2015. Complex background leaf-based plant identification method based on interactive segmentation and kernel descriptor. Proceedings of the 2nd International Workshop on Environmental Multimedia. In Conjunction with ACM Conference on Multimedia Retrieval (ICMR), Shanghai, China, 3–8. ACM.
  • Lin, M., Q. Chen, and S. Yan. 2014. Network in network. Arxiv Preprint Arxiv:1312.4400.
  • Mokhtar, U., N. El-Bendary, A. E. Hassenian, E. Emary, M. A. Mahmoud, H. Hefny, M. F. Tolba, U. Mokhtar, A. E. Hassenian, E. Emary, and M. A. Mahmoud. 2015. SVM-Based detection of tomato leaves diseases. In Advances in intelligent systems and computing, Eds., D. Filev, J. Jablkowski, J.Kacprzyk, M. Krawczak, I. Popchev, L. Rutkowski, V. Sgurev, E. Sotirova, P. Szynkarczyk, and S. Zadrozny, Vol. 323, 641–52. Springer, Cham, Switzerland.
  • Nechadi, S., F. Benddine, A. Moumen, and M. Kheddam. 2002. Etat des maladies virales de la tomate et strat{é}gie de lutte en Alg{é}rie. EPPO Bulletin 32:21–24.
  • Prasad, S., S. K. Peddoju, and D. Ghosh. 2016. Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal, Image and Video Processing 10:379–88. doi:10.1007/s11760-015-0751-y.
  • Raza, S. E. A., G. Prince, J. P. Clarkson, and N. M. Rajpoot. 2015. Automatic detection of diseased tomato plants using thermal and stereo visible light images. Plos ONE 10:e0123262. doi:10.1371/journal.pone.0123262.
  • Sannakki, S. S., V. S. Rajpurohit, V. B. Nargund, and P. Kulkarni. 2013. Diagnosis and classification of grape leaf diseases using neural networks. Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India, 3–7. IEEE.
  • Schikora, A. S. M. 2014. Image-based analysis to study plant infection with human pathogens. Computational and Structural Biotechnology Journal 12:1–6. doi:10.1016/j.csbj.2014.09.010.
  • Semary, N. A., A. Tharwat, E. Elhariri, and A. E. Hassanien. 2015. Fruit-based tomato grading system using features fusion and support vector machine. In Advances in intelligent systems and computing, Eds., D. Filev, J. Jablkowski, J.Kacprzyk, M. Krawczak, I. Popchev, L. Rutkowski, V. Sgurev, E. Sotirova, P. Szynkarczyk, and S. Zadrozny, Vol. 323, 401–10. Springer, Cham, Switzerland.
  • Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, USA,7–12 June, 1–9.
  • Xie, C., and Y. He. 2016. Spectrum and image texture features analysis for early blight disease detection on eggplant leaves. Sensors 16:676. doi:10.3390/s16050676.
  • Xie, C., Y. Shao, X. Li, and Y. He. 2015. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Scientific Reports 5:16564. doi:10.1038/srep16564.
  • Yosinski, J., J. Clune, A. Nguyen, T. Fuchs, and H. Lipson. 2015. Understanding neural networks through deep visualization. International Conference on Machine Learning - Deep Learning Workshop 2015:12.
  • Zeiler, M. D., and R. Fergus. 2014. Visualizing and understanding convolutional networks arXiv:1311.2901v3 [cs.CV] 28 Nov 2013. In Computer Visioneccv 2014, Vol. 8689, Eds., D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, 818–33. Springer, Boston, USA.

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