Mingming Lai∗,∗∗ and Lutao Gao∗,∗∗


  1. [1] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. Alrahamneh, Fast and accurate detection and classification of plant diseases, International Journal of Computer Applications, 17(1), 2011, 31–38.
  2. [2] D. Yang, A. Lu, and J. Wang, Classification of cooked beef, lamb, and pock using hyperspectral imaging, International Journal of Robotics and Automation, 33(3), 2018, 293–301.
  3. [3] N. Agrawal, J. Singhai, and D.K. Agarwa, Grape leaf disease detection and classification using multi-class support vector machine, International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE). IEEE, Bhopal, India, 2017, 238–244.
  4. [4] M. Ma, F. Chen, M. Guo, and Y.L. Chen, A recognition method based on improved LeNet-5 for street view house numbers, Journal of Yunnan University, 38(2), 2016, 197–203.
  5. [5] J. Amara, B. Bouaziz, and A. Algergawy, A deep learningbased approach for banana leaf diseases classification, Datenbanksysteme fr Business, Technologie und Web (BTW 2017)Workshopband, Stuttgart, Germany, 2017.
  6. [6] F.Z. Zhuang, P. Luo, Q. He, Z.Z. Shi, Research progress of transfer learning, Journal of Software, 26(1), 2015, 26–39.
  7. [7] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y.A. Nanehkaran, Using deep transfer learning for image-based plant disease identification, Computers and Electronics in Agriculture, 173, 2020, 105393.
  8. [8] V.K. Shrivastava, M.K. Pradhan, S. Minz, and M.P. Thakur, Rice plant disease classification using trnasfer learning of deep convolution neural network, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, New Delhi, India, 2019.
  9. [9] X.B. Shi, X.J. Fang, D.Y. Zhang, and Z.Q Guo, Image classification based on mixed deep learning model transfer learning, Journal of System Simulation, 28(1), 2016, 167–173.
  10. [10] K. Simonyan, and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: 1409.1556, 2014.
  11. [11] A. Krizhevsky, I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2012, 1097–1105.
  12. [12] B. Li, Q. Wang, and J. Hu, A fast SVM training method for very large datasets, International Joint Conference on Neural Networks. IEEE, Atlanta, GA, USA, 2009, 1784–1789.
  13. [13] Zghal N S, Kallel I K. An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification, 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). IEEE, Sousse, Tunisia, 2020, 1–6.
  14. [14] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA, 2016, 2818–2826.

Important Links:

Go Back