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.4 Coefficient of determination （R²） of crop yield prediction at different growth stages using various machine learning models

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.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.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

Founded in 1987
Superintendent: Ministry of Agriculture and Rural Affairs, PRC
Sponsored by: Chinese Society of Nuclear Agricultural Sciences;Institute of Food Science and Technology CAAS
ISSN：1000-8551
CN 11-2265/S

Address：No.2 of Yuan Ming Yuan Western Road, Haidian District, Beijing, China
Institute of Agro-Products Processing Science&Technology, Chinese Academy of Agricultural Sciences
Post Code：100193
Web Address：http://www.hnxb.org.cn
E-mail：henongxuebao@126.com
Tel：010-62815961

Visited

模态框（Modal）标题

Figure/Table detail