Acknowledgement
This work was supported by a Research promotion program of SCNU
References
- G. Lou , Y. Liu , T. Zhang, and X. Zheng., "STFL: A Spatial-temporal federated learning framework for graph neural networks," AAAI Conference on Artificial Intelligence Workshop on Deep Learning on Graphs: Methods and Applications, Vancouver, Canada, 2021.
- J. Fan, J. Bai, Z. Li, A. O. Bobea, and C. P. Gomes, ""A GNN-RNN approach for harnessing geospatial and temporal information: application to crop yield prediction," Proceedings of the AAAI conference on artificial intelligence, vol. 36, no. 11, 2022, pp. 11873-11881.
- C. Yang, H. Xie, L. Sun, L. He, L. Yang, S. S. Yu, Y. Rong, P. Zhao, and J. Huang, "Fedgraphnn: A federated learning benchmark system for graph neural networks," ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML), Appleton, USA, 2021.
- R. Liu, P. Xing, Z. Deng, A. Li, and C. Guan, "Federated graph neural networks: overview, techniques and challenges," Journal of latex class files, , vol. 14, no. 8, 2021, pp. 1-16.
- M. T. K. Makkithaya and N. V. G, "A Federated Learning-Based Crop Yield Prediction for Agricultural Production Risk Management," 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-7.
- P. S. M. Gopal and R. Bhargavi, "A novel approach for efficient crop yield prediction," Computers and Electronics in Agriculture," vol. 165, 2019, pp. 1-9. https://doi.org/10.1016/j.compag.2019.104968
- M. U. Ahmed and I. Hussain, "Prediction of wheat production using machine learning algorithms in northern areas of Pakistan," Telecommunications policy, vol. 46, Issue 6, 2022, pp. 1-12. https://doi.org/10.1016/j.telpol.2022.102370
- S. Yang, L. Gu, X. Li, T. Jiang, and R. Ren, "Crop classification method based on optimal feature selection and Hybrid CNN-RF networks for multi-temporal remote sensing imagery," Remote Sensing, vol. 12, no. 19, 2020, pp. 3119-3225. https://doi.org/10.3390/rs12193119
- S. Gupta, A. Geetha, K. S. Sankaran, and A. S. Zamani, "Machine learning- and feature selection-enabled framework for accurate crop yield prediction," Journal of Food Quality, vol. 2022, 2023, pp. 1-7. https://doi.org/10.1155/2022/6293985
- S. K. S. Durai and M. D. Shamili, "Smart farming using Machine learning and deep learning techniques," Decision Analytics Journal, vol. 2, no. 3, 2022, pp. 1-30. https://doi.org/10.1016/j.dajour.2022.100041
- O. Khin and S. Lee, "Performance Analysis of Deep Reinforcement Learning Algorithms in Agricultural Crop Production," J. of the Korea Institute of Electronic Communication Sciences, vol. 18, no. 1, 2023, pp. 99-105.
- J. Bong, S. Jeong, S. Jeong, and J. Han, "Study on Image Use for Plant Disease Classification," J. of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 2, 2022, pp. 343-350.
- O. Khin and S. Lee, " Feature Extraction and Recognition of Myanmar Characters Based on Deep Learning," J. of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 5, 2022, pp. 977-984.