Yunjie Zhang, Dong Ren, Bangqing Chen, and Jian Gu


  1. [1] S.Q. Fang and W.M. Chen, Application of aerial video techniqueon the monitoring of pine disease from Bursapheleachusxylophilus, China Forestry Science and Technology, 17(5),2003, 42–43.
  2. [2] Z.D. Yang, B.G. Zhao, and J. Guo, Review on behavior studiesof the pine wood nematode, Journal of Nanjing ForestryUniversity, 27(1), 2003, 87–92.
  3. [3] W.Z Xie, H.H Hhang, M.J Hhang, and Y.K Hhang, Occurrenceand control of forestry harmful organisms in GuangdongProvince, Journal of Environmental Entomology, 39(6), 2017,1191–1197.
  4. [4] X. Xinluo, T. Huan, L. Cunjun, C. Cheng, G. Hang, and Z.Jingping, Detection and location of pine wilt disease induceddead pine trees based on faster R-CNN, Transactions ofthe Chinese Society for Agricultural Machinery, 51(7), 2020,228–236. doi: 10.6041/j.issn.1000-1298.2020.07.026
  5. [5] B. Zoph and Q. V. Le, Neural architecture search with rein-forcement learning, Proc. Int. Conf. Learning Representations(ICLR), Toulon, 2017, 1–16.
  6. [6] B. Zoph, V. Vasudevan, J. Shlens, and Q.V. Le, Learningtransferable architectures for scalable image recognition, Proc.CVPR, Salt Lake City, UT, 2018, 8697–8710.
  7. [7] E. Real, A. Aggarwal, Y. Huang, and Q.V. Le, Regularizedevolution for image classifier architecture search, Proc. 33rdAAAI Conf. Artificial Intelligence, Salt Lake City, UT, 2018,4780–4789.
  8. [8] B. Baker, O. Gupta, N. Naik, and R. Raskar, Designing neuralnetwork architectures using reinforcement learning, Proc. Int.Conf. Learning Representations (ICLR), San Juan, 2016, 1–18.
  9. [9] G. Ghiasi, T.-Y. Lin, R. Pang, and Q.V. Le, NAS-FPN:Learning scalable feature pyramid architecture for objectdetection, arXiv:1904.07392v1, Apr. 2019.
  10. [10] K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learningfor image recognition, Proc. CVPR, Las Vegas, NV, 2016,770–778.
  11. [11] T.-Y. Lin, P. Doll´ar, R.B. Girshick, K. He, B. Hariharan, andS.J. Belongie, Feature pyramid networks for object detection,Proc. CVPR, Honolulu, HI, 2017, 936–944.
  12. [12] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Doll´ar, Focalloss for dense object detection, Proc. ICCV, Venice, 2017,2999–3007.
  13. [13] B. Zhang, H. Ye, W. Lu, and W. Huang, A spatiotemporalchange detection method for monitoring pine wilt diseasein a complex landscape using high-resolution remote sensingimagery, Remote Sensing, 13(11), 2021, 2083.
  14. [14] J. Wang, J. Zhao, H. Sun, X. Lu, and J. Huang, Satellite remotesensing identification of discolored standing trees for pinewilt disease based on semi-supervised deep learning, RemoteSensing, 14(23), 2022, 5936.
  15. [15] X. Li, Y. Liu, P. Huang, T. Tong, and L. Li, Integrating multi-scale remote-sensing data to monitor severe forest infestationin response to pine wilt disease, Remote Sensing, 14(20), 2022,5164.
  16. [16] R. Yu, Y. Luo, H. Li, L. Yang, H. Huang, and L. Yu, Three-dimensional convolutional neural network model for earlydetection of pine wilt disease using UAV-based hyperspectralimages, Remote Sensing, 13, 2021, 4065.245

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