您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery

文献类型： 外文期刊

作者： Shen, Yulin 1 ; Mercatoris, Benoit 2 ; Cao, Zhen 1 ; Kwan, Paul 3 ; Guo, Leifeng 1 ; Yao, Hongxun 4 ; Cheng, Qian 5 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

2.Univ Liege, TERRA Teaching & Res Ctr, Gembloux Agrobio Tech, Biosyst Dynam & Exchanges, B-5030 Gembloux, Belgium

3.Melbourne Inst Technol, 288 La Trobe St, Melbourne, Vic 3000, Australia

4.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China

5.Chinese Acad Agr Sci, Farmland Irrigat Res Inst, Henan Key Lab Water Saving Agr, Xinxiang 453002, Henan, Peoples R China

关键词： UAV; wheat yield; multispectral; thermal infrared; long short-term memory network

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 6 期

页码：

收录情况： SCI

摘要： Yield prediction is of great significance in agricultural production. Remote sensing technology based on unmanned aerial vehicles (UAVs) offers the capacity of non-intrusive crop yield prediction with low cost and high throughput. In this study, a winter wheat field experiment with three levels of irrigation (T1 = 240 mm, T2 = 190 mm, T3 = 145 mm) was conducted in Henan province. Multispectral vegetation indices (VIs) and canopy water stress indices (CWSI) were obtained using an UAV equipped with multispectral and thermal infrared cameras. A framework combining a long short-term memory neural network and random forest (LSTM-RF) was proposed for predicting wheat yield using VIs and CWSI from multi-growth stages as predictors. Validation results showed that the R-2 of 0.61 and the RMSE value of 878.98 kg/ha was achieved in predicting grain yield using LSTM. LSTM-RF model obtained better prediction results compared to the LSTM with R-2 of 0.78 and RMSE of 684.1 kg/ha, which is equivalent to a 22% reduction in RMSE. The results showed that LSTM-RF considered both the time-series characteristics of the winter wheat growth process and the non-linear characteristics between remote sensing data and crop yield data, providing an alternative for accurate yield prediction in modern agricultural management.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台