Author:
Xie Ziwen,Chen Song,Gao Guizhen,Li Hao,Wu Xiaoming,Meng Lei,Ma Yuntao
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Reference61 articles.
1. Aballa, A., Cen, H., Wan, L., Mehmood, K., & He, Y. (2020). Nutrient Status Diagnosis of Infield Oilseed Rape via Deep Learning-enabled Dynamic Model.IEEE Trans. Ind. Inform.1–1
2. Abdalla, A., Cen, H., Abdel-Rahman, E., Wan, L., & He, Y. (2019a). Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm. Remote Sens, 11, 3001
3. Abdalla, A., Cen, H., Wan, L., Rashid, R., Weng, H., Zhou, W., & He, Y. (2019b). Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure. Comput. Electron. Agric, 167, 105091
4. Abdalla, A., Cen, H., El-manawy, A., & He, Y. (2019c). Infield oilseed rape images segmentation via improved unsupervised learning models combined with supreme color features. Comput. Electron. Agric, 162, 1057–1068
5. Ahmed, O. S., Shemrock, A., Chabot, D., Dillon, C., Williams, G., Wasson, R., & Franklin, S. E. (2017). Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle. Int. J. Remote Sens, 38, 2037–2052
Cited by
4 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