Acknowledgement
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry(IPET) and Korea Smart Farm R&D Foundation(KosFarm) through Smart Farm Innovation Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA) and Ministry of Science and ICT(MSIT), Rural Development Administration(RDA) (421005-04)
References
- Dong, J.; Fuentes, A.; Yoon, S.; Kim, T.; Park, D.S, "Towards Improved Performance on Plant Disease Recognition with Symptoms Specific Annotation," Smart Media Journal, vol. 11, no.4, pp. 38-45, 2022.
- Dong, J.; Lee, J.; Fuentes, A.; Xu, M.; Yoon, S.; Lee, M.H.; Park, D.S. "Data-centric annotation analysis for plant disease detection: Strategy, consistency, and performance," Frontiers in Plant Science, vol. 13, 2022.
- Dong, J.; Fuentes, A.; Yoon, S.; Kim, H.; Jeong, Y.; Park, D.S, "A new deep learning-based dynamic paradigm towards open-world plant disease detection," Frontiers in Plant Science, vol. 14, 2023.
- Dong, J.; Fuentes, A.; Yoon, S.; Kim, H.; Park, D.S. "An iterative noisy annotation correction model for robust plant disease detection," Frontiers in Plant Science, vol. 14, 2023.
- Dong, J.; Fuentes, A. "Towards Improved Performance on Plant Disease Recognition with Symptoms Specific Annotation," Smart Media Journal, vol. 11, no.4 , pp. 38-45, 2022.
- Xu, M.; Yoon, S.; Lee, J.; Park, D.S. "Unsupervised transfer learning for plant anomaly recognition," Smart Media Journal, vol. 11, no. 4, pp. 30-37, 2022.
- Xu, M.; Yoon, S.; Park, J.; Baek, J.; Park, D.S. "Predicting Desired Fertigation for Rose Using Internet of Things Sensors and Time-Series Model," Smart Media Journal, vol. 13, no. 2, pp. 16-22, 2024.
- Zhong, Z.; Fini, E.; Roy, S.; Luo, Z.; Ricci, E.; Sebe, N. "Neighborhood contrastive learning for novel class discovery," In Proceedings of the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10867-10875, 2021.
- Cui, P.; Wang, J. "Out-of-distribution (ood) detection based on deep learning: A review," Electronics, vol. 11, no. 21, 2022.
- Meng, Y.; Xu, M.; Kim, H.; Yoon, S.; Jeong, Y.; Park, D.S, " Known and unknown class recognition on plant species and diseases," Computers and Electronics in Agriculture, vol. 215, 2023.
- Fuentes, A.; Yoon, S.; Kim, T.; Park, D.S. "Open set self and across domain adaptation for tomato disease recognition with deep learning techniques," Frontiers in Plant Science, vol. 12, 2021.
- Gou, J.; Yu, B.; Maybank, S.J.; Tao, D. "Knowledge distillation: A survey," International Journal of Computer Vision, vol. 129, pp. 1789-1819, 2021.
- Caron, M.; Touvron, H.; Misra, I.; Jegou, H.; Mairal, J.; Bojanowski, P.; Joulin, A. "Emerging properties in self-supervised vision transformers," In Proceedings of the Proceedings of the IEEE/CVF international conference on computer vision, pp. 9650-9660, 2021.
- Oquab, M.; Darcet, T.; Moutakanni, T.; Vo, H.; Szafraniec, M.; Khalidov, V.; Fernandez, P.; Haziza, D.; Massa, F.; El-Nouby, A. "Dinov2: Learning robust visual features without supervision," arXiv preprint arXiv:2304.07193 2023.
- Fini, E.; Sangineto, E.; Lathuiliere, S.; Zhong, Z.; Nabi, M.; Ricci, E, "A unified objective for novel class discovery," In Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9284-9292, 2021.
- Gu, P.; Zhang, C.; Xu, R.; He, X. "Class-relation knowledge distillation for novel class discovery," lamp, 2023.
- Tan, K.C.; Liu, Y.; Ambrose, B.; Tulig, M.; Belongie, S. "The herbarium challenge 2019 dataset," arXiv preprint arXiv:1906.05372 2019.
- Hughes, D.; Salathe, M. "An open access repository of images on plant health to enable the development of mobile disease diagnostics," arXiv preprint arXiv:1511.08060 2015.
- Kuhn, H.W. "The Hungarian method for the assignment problem," Naval research logistics quarterly, vol. 2, no. 1-2, pp. 83-97, 1955.