과제정보
This work was supported by the 2021 CAET Smart Campus Project (No. C21ZD02).
참고문헌
- N. R. Bhimte and V. R. Thool, "Diseases detection of cotton leaf spot using image processing and SVM classifier," in Proceedings of 2018 2nd International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 340-344. https://doi.org/10.1109/ICCONS.2018.8662906
- D. A. Padilla, G. V. Magwili, A. L. A. Marohom, C. M. G. Co, J. C. C. Gano, and J. M. U. Tuazon, "Portable yellow spot disease identifier on sugarcane leaf via image processing using support vector machine," in Proceedings of 2019 5th International Conference on Control, Automation and Robotics (ICCAR), Beijing, China, 2019, pp. 901-905. https://doi.org/10.1109/ICCAR.2019.8813495
- P. Sharma, P. Hans, and S. C. Gupta, "Classification of plant leaf diseases using machine learning and image preprocessing techniques," in Proceedings of 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2020, pp. 480-484. https://doi.org/10.1109/Confluence47617.2020.9057889
- C. Zhou, J. Song, S. Zhou, Z. Zhang, and J. Xing, "COVID-19 detection based on image regrouping and ResNet-SVM using chest X-ray images," IEEE Access, vol. 9, pp. 81902-81912, 2021. https://doi.org/10.1109/ACCESS.2021.3086229
- M. Z. Hasan, M. S. Ahamed, A. Rakshit, and K. Z. Hasan, "Recognition of jute diseases by leaf image classification using convolutional neural network," in Proceedings of 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 2019, pp. 1-5. https://doi.org/10.1109/ICCCNT45670.2019.8944907
- J. G. Barbedo, "Factors influencing the use of deep learning for plant disease recognition," Biosystems Engineering, vol. 172, pp. 84-91, 2018. https://doi.org/10.1016/j.biosystemseng.2018.05.013
- S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. Sophia, and B. Pavithra, "Tomato leaf disease detection using deep learning techniques," in Proceedings of 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 979-983. https://doi.org/10.1109/ICCES48766.2020.9137986
- C. Zhou, S. Zhou, J. Xing, and J. Song, "Tomato leaf disease identification by restructured deep residual dense network," IEEE Access, vol. 9, pp. 28822-28831, 2021. https://doi.org/10.1109/ACCESS.2021.3058947
- Q. H. Cap, H. Uga, S. Kagiwada, and H. Iyatomi, "LeafGAN: an effective data augmentation method for practical plant disease diagnosis," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 2, pp. 1258-1267, 2022. https://doi.org/10.1109/TASE.2020.3041499
- P. Zhao, T. Wu, S. Zhao, and H. Liu, "Robust transfer learning based on geometric mean metric learning," Knowledge-Based Systems, vol. 227, article no. 107227, 2021. https://doi.org/10.1016/j.knosys.2021.107227
- S. Coulibaly, B. Kamsu-Foguem, D. Kamissoko, and D. Traore, "Deep neural networks with transfer learning in millet crop images," Computers in Industry, vol. 108, pp. 115-120, 2019. https://doi.org/10.1016/j.compind.2019.02.003
- A. Abd Almisreb, N. Jamil, and N. M. Din, "Utilizing AlexNet deep transfer learning for ear recognition," in Proceedings of 2018 4th International Conference on Information Retrieval and Knowledge Management (CAMP), Kota Kinabalu, Malaysia, 2018, pp. 1-5. https://doi.org/10.1109/INFRKM.2018.8464769
- X. Zhang, J. Zou, K. He, and J. Sun, "Accelerating very deep convolutional networks for classification and detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 10, pp. 1943-1955, 2016. https://doi.org/10.1109/TPAMI.2015.2502579
- T. Emara, H. M. Afify, F. H. Ismail, and A. E. Hassanien, "A modified Inception-v4 for imbalanced skin cancer classification dataset," in Proceedings of 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, 2019, pp. 28-33. https://doi.org/10.1109/ICCES48960.2019.9068110
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778. https://doi.org/10.1109/CVPR.2016.90
- G. Huang, Z. Liu, L. Van Der Maaten, and K. G. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017, pp. 2261-2269. https://doi.org/10.1109/CVPR.2017.243
- Stanford Vision Lab, "ImageNet large scale visual recognition challenge 2012 (ILSVRC2012)," 2020 [Online]. Available: https://image-net.org/challenges/LSVRC/2012/.
- J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640-651, 2017. https://doi.org/10.1109/TPAMI.2016.2572683