| Cite this article: | 李宗鹏,陈震,程千,等.基于Stacking法的无人机光谱遥测冬小麦产量[J].灌溉排水学报,0,():-. |
| LI Zongpeng,chenzhen,chengqian,et al.基于Stacking法的无人机光谱遥测冬小麦产量[J].灌溉排水学报,0,():-. |
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| DOI： |
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| UAV spectrum remote measurement of winter wheat yield based on Stacking method |
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LI Zongpeng,chenzhen,chengqian,et al
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1.College of mechanical and electrical engineering, Henan Agricultural University;2.Institute of irrigation, Chinese Academy of Agricultural Sciences
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| Abstract: |
| In order to accurately and efficiently predict crop yields, this paper takes winter wheat as the research object and uses drones equipped with multispectral cameras to obtain multispectral image data of the heading, flowering, and filling periods.Select 14 vegetation indices that are sensitive to yield according to multispectral bands, and select 13 indices that are extremely significantly related to yield;The 10-fold cross-validation method was used to establish MLR (Multiple Linear Regression), PLSR (Partial Least Squares Regression), SVM (Support Vector Machine) and Cubist yield estimation primary models for each growth stage for comparative analysis, and stacking method integration The primary model establishes the MLR and Cubist secondary production estimation models for each period.The results show that the Cubist model in the primary learner is in the heading stage (R2 = 0.41, RMSE = 1.21t ha-1, NRMSE = 18.59%), flowering stage (R2 = 0.45, RMSE = 1.19t ha-1, NRMSE = 18.23%) and During the grouting period (R2 = 0.57, RMSE = 1.07t ha-1, NRMSE = 16.36%), the accuracy of production estimation is the highest;When MLR is used as a secondary learner, R2 is increased by 0.11, 0.10, and 0.04, respectively, than the highest value of each growth period of the primary learner, and the RMSE is reduced by 0.10t ha-1, 0.12t ha-1, and 0.02t ha-1 respectively from the lowest value. When Cubist is used as a secondary learner, R2 is increased by 0.12, 0.13, and 0.04, respectively, than the maximum value of each growth period of the primary learner, and the RMSE is reduced by 0.11 t ha-1, 0.14 t ha-1, and 0.02 t ha-1, respectively, from the lowest value. And Cubist is used as a secondary learner in the heading stage (R2 = 0.54, RMSE = 1.12 t ha-1), flowering stage (R2 = 0.58, RMSE = 1.06 t ha-1) and filling stage (R2 = 0.61, RMSE = 1.05 t ha-1) The yield estimation accuracy is still the highest.Using the Stacking method to integrate different primary learner models can effectively improve the accuracy of yield estimation, which can provide a reference for future winter wheat yield estimation research. |
| Key words: Multispectral; vegetation index; ten-fold cross-validation; Stacking; learner |
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