Estimating grain filling rate (GFR) and thousand-grain weight (TGW) plays an important role in evaluating yield and guiding the selection of varieties and cultivation strategies of winter wheat (). However, the current GFR and TGW monitoring methods mainly rely on destructive sampling, which can not achieve rapid estimation in a large area of farmland. This study aims to establish a method for estimating GFR and TGW of winter wheat using multispectral UAV images. Initially, grey correlation analysis method was used to evaluate the contributions of Leaf Area Index (LAI), Chlorophyll Content (SPAD), Aboveground Biomass (AGB) to GFR. A new comprehensive indicator, called LAI-SPAD-AGB index (LSA), was proposed to characterize GFR by establishing a linear regression model between LSA and GFR. Subsequently, UAV-based multispectral images were used to estimate LAI, SPAD, AGB, employing the methods such as Partial Least Squares Regression (PLSR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Using the linear regression equation between LSA and GFR along with estimated LSA values, GFR was estimated and mapped. TGW was estimated based on GFR and grain-filling duration (GFD). Results showed the high GFR estimation accuracy (R: 0.89, RMSE: 0.29 g/d, NRMSE: 10.0 %) and remarkable TGW estimation precision (R: 0.92, RMSE: 4.20 g, NRMSE: 8.1 %). The parcel-scale distribution maps of estimated GFR and TGW were generated. The novel and non-destructive method of estimating GFR and TGW of winter wheat using UAV-based images can offer strong support for water and fertilizer management in the field.

中文翻译：

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使用基于无人机的多光谱图像估算冬小麦 (Triticum aestivum L.) 的籽粒灌浆率和千粒重


估算籽粒灌浆率（GFR）和千粒重（TGW）对于评价冬小麦产量、指导品种选择和栽培策略具有重要作用。但目前的GFR和TGW监测方法主要依靠破坏性采样，无法实现大面积农田的快速估算。本研究旨在建立一种利用多光谱无人机图像估算冬小麦 GFR 和 TGW 的方法。最初采用灰色关联分析法评价叶面积指数(LAI)、叶绿素含量(SPAD)、地上生物量(AGB)对GFR的贡献。通过建立LSA和GFR之间的线性回归模型，提出了一种新的表征GFR的综合指标，称为LAI-SPAD-AGB指数（LSA）。随后，利用基于无人机的多光谱图像，采用偏最小二乘回归（PLSR）、随机森林（RF）和极限梯度提升（XGBoost）等方法来估计LAI、SPAD、AGB。使用 LSA 和 GFR 之间的线性回归方程以及估计的 LSA 值，估计并绘制 GFR。 TGW 是根据 GFR 和灌浆持续时间 (GFD) 估算的。结果显示，GFR 估计精度较高（R：0.89，RMSE：0.29 g/d，NRMSE：10.0 %），TGW 估计精度显着（R：0.92，RMSE：4.20 g，NRMSE：8.1 %）。生成了估计 GFR 和 TGW 的地块尺度分布图。利用无人机图像估算冬小麦 GFR 和 TGW 的新颖且无损的方法可以为田间水肥管理提供有力支持。