Open Access
Issue
E3S Web Conf.
Volume 248, 2021
2021 3rd International Conference on Civil Architecture and Energy Science (CAES 2021)
Article Number 03080
Number of page(s) 7
Section Research on Civil Water Conservancy Engineering and Urban Architecture
DOI https://doi.org/10.1051/e3sconf/202124803080
Published online 12 April 2021
  1. Sun Z J. Shaanxi has become the first apple province in China [J]. China fruit industry information, 2008(10):34. [Google Scholar]
  2. Xin Q R. Research on the information extraction of Apple Garden in mountainous area based on multi-temporal high-resolution image [D]. Shandong University of Technology, 2017. [Google Scholar]
  3. Meng L H. Cotton field yield estimation model based on time series remote sensing image [D]. Northeast Agricultural University, 2018. [Google Scholar]
  4. Li S J, Sun Y N, Li M, Chen Z W. Advances in crop yield estimation by remote sensing at home and abroad [J]. The world’s agricultural, 2013(05):125-127+131+156. [Google Scholar]
  5. Wang D P, Wang Z L, Li D Y. Information Recognition of SPOT-5 Image Orchard Based on NDVI Texture in Hilly Area of Shandong Province [J]. Science of Surveying and Mapping, 2007(01):126-127+121+165. [Google Scholar]
  6. Zhang A D, Peng D M, Li D Y, Wang Z L, Wang D P. Research on Extraction Technology of Orchard Spatial Information Based on TM Image [J]. Science of Surveying and Mapping, 2007(05):121-123+205. [Google Scholar]
  7. Luo W, Kuang R Y. Extraction of Orchard Information from Environmental Satellite Images in the Headwaters of Dongjiang River [J]. Science of Surveying and Mapping, 2014, 39(08):135-139. [Google Scholar]
  8. Liu J Q. Research on information extraction of apple orchard in fufeng county based on Landsat8 remote sensing image [D]. Northwest Agriculture & Forestry University, 2015. [Google Scholar]
  9. Guo B, Wu Y Y, Zhou J Y, Wang ZZ, Shi Q Y, Tian Y. Simulation of the apple yield based on unmanned aerial vehicle image data ———Taking Luochuan county as an example [J]. Journal of Xi’an University of Science and Technology, 2017, 37(03):450-454. [Google Scholar]
  10. Dong F. Research on multi-source data based remote sensing extraction technology of Apple Garden information in hilly Area [D]. Shandong Agricultural University, 2012. [Google Scholar]
  11. Xu H Z Y, Liu C, Qi S H, Zhao G S. Remote sensing information extraction of citrus orchard in southern Jiangxi based on random forest algorithm [J]. Journal of Jiangxi Normal University (Natural Science), 2018, 42(04):434-440. [Google Scholar]
  12. Zhang L, Gong Z N, Wang Q W, et al. Sentinel-2 Image Multi-feature Optimized Information extraction of Yellow River Delta Wetland [J]. Journal of remote sensing, 2019, 23(02): 313-326. [Google Scholar]
  13. Zhong Y. Information Extraction of Citrus Orchard Based on Improved Support Vector Machine and Spatial Distribution Characteristic Analysis [D]. Jiangxi University of Science and Technology, 2019. [Google Scholar]
  14. Su T, Wang P X, Liu X K, Yang B. Spring maize yield estimation based on entropy combined prediction and multi-temporal remote sensing [J]. 6/5000 Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(01):186-192. [Google Scholar]
  15. Franch B, Vermote E F, Becker-Reshef I, et al. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information [J]. Remote Sensing of Environment 2015, 161: 131-148. [Google Scholar]
  16. Liu W T, Kogan F. Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices[J]. International Journal of Remote Sensing, 2002, 23(6):1161-1179. [Google Scholar]
  17. Johnson D M. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States [J]. Remote Sensing of Environment, 2014, 141: 116-128. [Google Scholar]
  18. Moriondo M, Maselli F, Bindi M. A simple model of regional wheat yield based on NDVI data [J]. European Journal of Agronomy, 2007, 26(3): 266-274. [Google Scholar]
  19. Nuarsa I W, Nishio F, Hongo C, et al. Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia [J]. International Journal of Remote Sensing, 2012, 33(17): 5402-5417. [Google Scholar]
  20. Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24-31. [Google Scholar]
  21. Gislason P O, Benediktsson J A, Sveinsson J R. Random forest classification of multisource remote sensing and geographic data[C]. 2004 IEEE International Geoscience and Remote Sensing Symposium, 2004, 2: 1049-1052. [Google Scholar]
  22. B. Schölkopf, P. Bartlett, A. Smola, and R. Williamson, “Shrinking the tube: new support vector regression algorithm,” presented at the Proceedings of the 1998 conference on Advances in neural information processing systems II, 1999. [Google Scholar]
  23. Wang C, Liu Z, Yan C. An experimental study on imaging spectrometer data feature selection and wheat type identification[J]. Journal of remote sensing, 2006, 10(2): 24 [Google Scholar]

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