Dynamic prediction of carbon and nitrogen accumulation in winter wheat grain: Source-sink theory integrated with UAV multispectral imagery

Abstract

Timely monitoring of grain carbon and nitrogen accumulation dynamics is crucial for the growth monitoring and efficient field management of winter wheat. However, traditional destructive sampling methods are time-consuming, costly, and challenging to implement for large-scale rapid monitoring. Based on the source-sink theory, this study proposes a new method for predicting the dynamic changes of grain carbon and nitrogen accumulation in winter wheat grain by leveraging UAV-based inversion of agronomic parameters (APs). Remotely sensed aboveground biomass and plant nitrogen accumulation were employed as source indicators during the anthesis stage, along with the days after anthesis represented by phenological indices, as co-input variables for the model. Piecewise ordinary least squares regression was employed to analyze the temporal dynamics of grain carbon and nitrogen sink indicators. Combining feature selection and machine learning algorithms, the study developed a UAV multi-spectral image-driven APs inversion framework and visualized relevant grain indicators. The UAV-based grain carbon and nitrogen accumulation prediction model demonstrated excellent performance, with the grain weight accumulation showing R² = 0.86, nRMSE = 21.96 %, and RPD = 2.63; for grain nitrogen accumulation, R² = 0.71, nRMSE = 34.11 %, and RPD = 1.87; and for grain nitrogen content, R² = 0.70, nRMSE = 20.93 %, and RPD = 1.82. The grain carbon and nitrogen accumulation prediction model based on UAV multispectral images enables non-destructive prediction of the entire filling period, and has showcasing high accuracy and application potential.

Plant phenotyping relevance

UAVマルチスペクトル画像から冬コムギ粒の炭素・窒素蓄積などの形質を非破壊推定するモデルと反転フレームワークが研究の中心であり、性能評価も実施している。

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