# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
###### Abstract  
Wheat is the main grain crop in our country, and the traditional wheat yield estimation method is time-consuming and laborious. By estimating wheat yield efficiently, quickly and non-destructively, agricultural producers can quickly obtain information about wheat yield, manage wheat fields more scientifically and accurately, and ensure national food security. Taking the Xinxiang Experimental Base of the Crop Science Research Institute, Chinese Academy of Agricultural Sciences as an example, hyperspectral data for the critical growth stages of wheat were pre-processed. A total of 27 vegetation indices were calculated from the experimental plots. These indices were then subjected to correlation analysis with measured wheat yield. Vegetation indices with Pearson correlation coefficients greater than 0.5 were selected. Five methods, including multiple linear regression, stepwise regression, principal component regression, neural networks and random forests, were used to construct wheat yield estimation models. Among the methods used, multiple linear regression, stepwise regression and the models developed using principal component analysis showed a lower modelling accuracy and validation precision. However, the neural network and random forest methods both achieved a modelling accuracy R\({}^{2}\) greater than 0.6, with validation accuracy R\({}^{2}\) values of 0.729 and 0.946, respectively. In addition, the random forest method had a lower cross-validation RMSE value, with values of 869.8 kg/hm\({}^{-2}\), indicating a higher model accuracy. In summary, the random forest method provided the optimal estimation for wheat yield, enabling the timely and accurate pre-harvest wheat yield prediction, which has significant value for precision agriculture management and decision making.  
vegetation index; multiple regression; principal component regression; neural network; random forest +
Footnote †: journal: Medical  
yield estimation models, while others have combined crop growth conditions [12; 13] and physiological indicators. Although these studies have shown promising results for yield estimation, there is still room to optimize approaches for constructing wheat yield estimation models [12]. Using winter wheat in Xinxiang, Henan Province as a case study, this study pre-processed hyperspectral data collected during the wheat grain-filling stage [14] and calculated vegetation indices [15]. Correlation analysis was conducted between these indices and yield [16] to select the most suitable ones. Five modeling methods, including multiple linear regression (MLR) [17], stepwise multiple linear regression (SMLR), principal component analysis (PCA), artificial neural networks (ANN) and random forests (RF), were employed [14; 18; 19]. The study used 2/3 of the data (20 plots) for model development and reserved the remaining 1/3 (10 plots) for model validation to assess the accuracy of the wheat yield estimation models [20; 21; 22]. A comprehensive analysis was performed to evaluate the modeling and validation precision of each method and determine the most effective model for wheat yield estimation.