Fine-grained Potato Disease Identification Based on Contrastive Convolutional Neural Networks

References

• Agarap, A. F. 2018. Deep learning using rectified linear units (relu). arXiv preprint arXiv:180308375. doi:10.48550/arXiv.1803.08375.
   Google Scholar
• Andreas, K., and F. X. Prenafeta-Boldĺš. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70–314. doi:10.1016/j.compag.2018.02.016.
   Web of Science ®Google Scholar
• Brutzkus, A., and A. Globerson. 2021. An optimization and generalization analysis for max-pooling networks. Uncertainty in Artificial Intelligence, Online, 1650–60. PMLR.
   Google Scholar
• Camuto, A., M. Willetts, U. Simsekli, S. J. Roberts, and C. C. Holmes. 2020. Explicit regularisation in gaussian noise injections. Advances in Neural Information Processing Systems 33:16603–14.
   Google Scholar
• Chen, X., and H. Kaiming 2021. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15750–58. doi:10.48550/arXiv.2011.10566.
   Google Scholar
• Chen, T., S. Kornblith, M. Norouzi, and G. Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning, Virtual, 1597–607. PMLR.
   Google Scholar
• Chen, J., Q. Liu, and L. Gao. 2019. Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 11 (3):343. doi:10.3390/sym11030343.
   Google Scholar
• Devlin, J., M.W. Chang, K. Lee, and K. Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805. doi:10.48550/arXiv.1810.04805.
   Google Scholar
• Ding, X., C. Xia, X. Zhang, X. Chu, J. Han, and G. Ding. 2021. Repmlp: Re-parameterizing convolutions into fully-connected layers for image recognition. arXiv preprint arXiv:210501883. doi:10.48550/arXiv.2105.01883.
   Google Scholar
• Dongyu, Q. 2022. Doubling global potato production in 10 years is possible. https://www.fao.org.
   Google Scholar
• Geng, Z., Q. Meng, J. Bai, J. Chen, Y. Han, Q. Wei, and Z. Ouyang. 2019. A model-free bayesian classifier. Information Sciences 482:171–88. doi:10.1016/j.ins.2019.01.026.
   Web of Science ®Google Scholar
• Goodfellow, I., J. Pouget-Abadie, M. Mirza, X. Bing, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems 27. doi:10.1145/3422622.
   Google Scholar
• Grill, J. B., F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. Guo, M. G. Azar, et al. 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems 33:21271–84.
   Google Scholar
• Gu, J., Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, et al. 2018. Recent advances in convolutional neural networks. Pattern recognition 77:354–77. doi:10.1016/j.patcog.2017.10.013.
   Web of Science ®Google Scholar
• Hao, P.Y., J.H. Chiang, and Y.D. Chen. 2022. Possibilistic classification by support vector networks. Neural Networks 149:40–56. doi:10.1016/j.neunet.2022.02.007.
   PubMed Web of Science ®Google Scholar
• Hossain, S., R. Mumtahana Mou, M. Mahedi Hasan, S. Chakraborty, and M. Abdur Razzak. 2018. Recognition and detection of tea leaf’s diseases using support vector machine. In 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), 150–54. IEEE. doi:10.1109/CSPA.2018.8368703.
   Google Scholar
• Junde, C., J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran. 2020. Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture 173:105393. doi:10.1016/j.compag.2020.105393.
   Web of Science ®Google Scholar
• Kaiming, H., H. Fan, W. Yuxin, S. Xie, and R. Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 9729–38. doi:10.48550/arXiv.1911.05722.
   Google Scholar
• Kaiming, H., G. Gkioxari, P. Dollár, and R. Girshick. 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, 2961–69. doi:10.48550/arXiv.1703.06870.
   Google Scholar
• Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, 770–78.
   Google Scholar
• Khosla, P., P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan. 2020. Supervised contrastive learning. Advances in Neural Information Processing Systems 33:18661–73.
   Google Scholar
• Kiani, E., and T. Mamedov. 2017. Identification of plant disease infection using soft-computing: Application to modern botany. Procedia computer science 120:893–900. doi:10.1016/j.procs.2017.11.323.
   Google Scholar
• Krizhevsky, A., I. Sutskever, G. E. Hinton, and E. P. Simoncelli. 2012. Efficient and direct estimation of a neural subunit model for sensory coding. Advances in Neural Information Processing Systems 25:3113–21. doi:10.1145/3065386.
   PubMedGoogle Scholar
• Liang, X., L. Wu, J. Li, Y. Wang, Q. Meng, T. Qin, W. Chen, M. Zhang, T. Y. Liu. 2021. R-drop: Regularized dropout for neural networks. Advances in Neural Information Processing Systems 34:10890–905.
   Google Scholar
• Li, K., J. Lin, J. Liu, and Y. Zhao. 2020. Using deep learning for image-based different degrees of ginkgo leaf disease classification. Information 11 (2):95. doi:10.3390/info11020095.
   Google Scholar
• Lu, Y., H. Yifan, and J. Xiao. 2019. Help LabelMe: A fast auxiliary method for labeling image and using it in ChangE’s CCD data. In International Conference on Image and Graphics, 801–10. Springer. doi:10.1007/978-3-030-34120-665.
   Google Scholar
• Meng, R., S. G. Rice, J. Wang, and X. Sun. 2018. A fusion steganographic algorithm based on faster R-CNN. Computers, Materials & Continua 55 (1):1–16. doi:10.3970/cmc.2018.055.001.
   Web of Science ®Google Scholar
• Nasr-Esfahani, M. 2022. An IPM plan for early blight disease of potato alternaria solani sorauer and A. alternata (fries.) Keissler. Archives of Phytopathology and Plant Protection 55 (7):785–96. doi:10.1080/03235408.2018.1489600.
   Web of Science ®Google Scholar
• Nazki, H., S. Yoon, A. Fuentes, and D. Sun Park. 2020. Unsupervised image translation using adversarial networks for improved plant disease recognition. Computers and Electronics in Agriculture 168:105117. doi:10.1016/j.compag.2019.105117.
   Web of Science ®Google Scholar
• Niklaus, S., and F. Liu. 2020. Softmax splatting for video frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, 5437–46.
   Google Scholar
• Ning, Y., Y. Qian, H. S. EL-Mesery, R. Zhang, A. Wang, and J. Tang. 2019. Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree–confusion matrix method. Journal of the Science of Food and Agriculture 99 (14):6589–600. doi:10.1002/jsfa.9943.
   PubMed Web of Science ®Google Scholar
• Sari, E., M. Belbahri, and V. Partovi Nia. 2019. How does batch normalization help binary training? arXiv preprint arXiv:190909139 arXiv preprint arXiv:190909139. doi:10.48550/arXiv.1909.09139.
   Google Scholar
• Sharma, S., and M. Lal. 2022. Advances in Management of Late Blight of Potato 163–84. Springer. 10.1007/978-981-16-7695-67
   Google Scholar
• Shorten, C., and T. M. Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6 (1):1–48. doi:10.1186/s40537-019-0197-0.
   Google Scholar
• Simonyan, K., and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. doi:10.48550/arXiv.1409.1556.
   Google Scholar
• Sinaga, K. P., and M.S. Yang. 2020. Unsupervised K-means clustering algorithm. IEEE Access 8:80716–27. doi:10.1109/ACCESS.2020.2988796.
   Google Scholar
• Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, 2818–26.
   Google Scholar
• Yang, L., Y. Shujuan, N. Zeng, Y. Liu, and Y. Zhang. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–84. doi:10.1016/j.neucom.2017.06.023.
   Web of Science ®Google Scholar
• Yan, Y., L. Rumei, S. Wang, F. Zhang, W. Wei, and X. Weiran. 2021. Consert: A contrastive framework for self-supervised sentence representation transfer. arXiv preprint arXiv:210511741. doi:10.48550/arXiv.2105.11741.
   Google Scholar
• Yuen, J. 2021. Pathogens which threaten food security: Phytophthora infestans, the potato late blight pathogen. Food Security 13 (2):247–53. doi:10.1007/s12571-021-01141-3.
   Web of Science ®Google Scholar
• Zhang, Q. 2022. A novel ResNet101 model based on dense dilated convolution for image classification. SN Applied Sciences 4 (1):1–13. doi:10.1007/s42452-021-04897-7.
   PubMed Web of Science ®Google Scholar