UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation

Lulu Zhang, Xiaowen Wang, Huanhuan Zhang, Bo Zhang, Jin Zhang, Xinkang Hu, Xintong Du, Jianrong Cai, Weidong Jia and Chundu Wu ()
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Lulu Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xiaowen Wang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Huanhuan Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Bo Zhang: School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
Jin Zhang: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xinkang Hu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Xintong Du: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Jianrong Cai: School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Weidong Jia: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Chundu Wu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2024, vol. 14, issue 11, 1-26

Abstract: Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R 2 ) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

Keywords: UAV; comprehensive growth index; growth monitoring; winter wheat; vegetation index (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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