Author:
Siva Krishna P.,Mariam Bee M. K.
Reference5 articles.
1. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973)
2. Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Indian Conference on Computer Vision, Graphics and Image Processing, pp. 722–729 (2008)
3. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
4. Chollet, F.: Xception: deep learning with depth wise separable convolutions. arXiv:1610.02357 [cs.CV] (2016)
5. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integratedrecognition, localization and detection using convolutional networks. In: ICLR (2014)
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Hybrid CNN & Random Forest Model for Effective Marigold Leaf Disease Diagnosis;2024 International Conference on E-mobility, Power Control and Smart Systems (ICEMPS);2024-04-18
2. Hybrid CNN & Random Forest Model for Effective Clove Leaf Disease Dignosis;2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21
3. Hybrid CNN & Random Forest Model for Effective Ashwagandha Leaf Disease Diagnosis;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29