Journal of Nuclear Agricultural Sciences

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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

植被指数 Vegetation index	计算公式 Formulation
绿归一化差值植被指数 Green normalized difference vegetation index（GNDVI）	（NIR-G）/（NIR+G）
归一化差值植被指数 Normalized difference vegetation index（NDVI）	（NIR-R）/（NIR+R）
归一化差值红边指数 Normalized difference rededge（NDRE）	（NIR-RE）/（NIR+RE）
比值植被指数 Ratio vegetation index（RVI）	NIR/R
优化土壤调整植被指数 Optimized soil-adjusted vegetation index（OSAVI）	（NIR-R）/（NIR+R+0.16）
重归一化土壤调整植被指数 Renormalized soil-adjusted vegetation index（RESAVI）	1.5×（NIR-RE）/（NIR+RE+0.5）
改进型变换植被指数 Modified transformed vegetation index（MTVI）	1.2×［1.2×（NIR-G）-2.5×（R-G）］
增强型植被指数2 Enhanced vegetation index 2（EVI2）	2.5×（NIR-R）/（NIR+2.4×R+1）
改进型土壤调整植被指数 Modified soil-adjusted vegetation index（MSAVI）	｛2×NIR+1-sqrt［（2×NIR+1）²-8×（NIR-RED）］｝/2
变换叶绿素吸收比值指数 Transformed chlorophyll absorption ratio index（TCARI）	3×［（RE-R）-0.2×（RE-G）×（RE/R）］
绿红比指数 Green-red ratio index（GRRI）	G/R
绿蓝比指数 Green-Blue ratio index（GBRI）	G/B
红蓝比指数 Red-blueratio index（RBRI）	R/B
总体差异响应植被指数 Overall differential response vegetation index（ODRVI）	［（NIR-R）/（NIR+R）］×［（NIR-G）/（NIR+G）］

Table 2 Multispectral vegetation indices used in the study

Other figure/table from this article

Fig.1 Test location map
Table 1 Drone field data collection schedule
Fig.2 Flowchart of establishing yield estimation model with multispectral data
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.
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