Journal of Nuclear Agricultural Sciences

Figure/Table detail

Winter Wheat Yield Prediction Using UAV-Based Multispectral Time-Series Imagery

WAN Guoliang, WANG Yinting, LI Lei, WANG Duoxia, TAO Zhiqiang, SUN Haiyan

Journal of Nuclear Agricultural Sciences, 2026, 40(2): 413-422. DOI: 10.11869/j.issn.1000-8551.2026.02.0413

Fig.3 Correlation between multi-spectral bands and vegetation index and measured yield in the plot |r|
Note： Ⅰ： Elongation stage. Ⅱ： Flag leaf stage. Ⅲ： Flowering stage. Ⅳ： Early grain-filling stage. Ⅴ： Middle grain-filling stage. Ⅵ： Late grain-filling stage. Ⅶ： Wax-ripening stage.

Other figure/table from this article

Fig.1 Test location map
Table 1 Drone field data collection schedule
Table 2 Multispectral vegetation indices used in the study
Fig.2 Flowchart of establishing yield estimation model with multispectral data
Fig.4 Coefficient of determination （R²） of crop yield prediction at different growth stages using various machine learning models
Fig.5 Root RMSE and MAE of crop yield prediction at different growth stages using various machine learning models
Fig.6 Violin diagram of yield prediction values cross-validated by different machine learning models
Note： n.s.： Significance levels are P>0.05.
Fig.7 Scatter plots of predicted vs. observed yield values for each model
Note： A， B， C， D， E， F， and G represent the LSTM， RNN， RF， SVR， KNN， XGBoost， and RR models， respectively. The gray dashed line represents the 1∶1 line between the actual production and the model prediction， the red line is the fitted line for the observed and predicted values， RMSE and MAE units are t·hm^-2.
Fig.8 Scatter plots of predicted vs. observed yield values based on stratified sampling method
Note： A， B， C， D， E， F， and G represent the LSTM， RNN， RF， SVR， KNN， XGBoost， and RR models， respectively. The gray dashed line represents the 1∶1 line between the actual production and the model prediction， the red line is the fitted line for the observed and predicted values， RMSE and MAE units are t·hm^-2.
Fig.9 Comparison of prediction accuracy of machine learning models using random sampling and stratified sampling methods

模态框（Modal）标题

Figure/Table detail