# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
###### Abstract  
Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, antibasis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R\({}^{2}\) of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R\({}^{2}\) = 0.74), and the R\({}^{2}\) of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops. grain yield; hyperspectral vegetation index; leaf area index; elastic network; stacking technology 20211112164. [[https://doi.org/10.3390/app112412164](https://doi.org/10.3390/app112412164)]([https://doi.org/10.3390/app112412164](https://doi.org/10.3390/app112412164))
and large spectral information contents [13]. In recent years, scholars at home and abroad have carried out research and made progress in yield estimation of various crops using remote sensing technology [14; 15; 16]. To the best of our knowledge, most current studies construct GY prediction models by using a single index such as spectral reflectance, spectral index, or vegetation index. However, as the plants grow, spectral saturation occurs when the crop coverage is high, making it difficult to continue improving GY prediction accuracy. The essence of crop yield formation is the continuous accumulation of dry matter produced by photosynthesis in the plant body [17; 18]. It was found that LAI determines many biophysical processes, such as photosynthesis, respiration, and transpiration in crops [19; 20]. LAI is used as a common monitoring variable during crop growth and development and has a close relationship with final crop yield [21]. For example, [PERSON] et al. [22] used UAV-based hyperspectral remote sensing data to evaluate correlations between soybean yield and physiological and ecological parameters such as plant height, LAI, fresh biomass, and dry biomass, and identified LAI and fresh biomass as key indicators of soybean yield. [PERSON] et al. [23] proved that LAI was closely related to GY by studying the influence of LAI at different growth stages of wheat on GY estimation and concluded that LAI can be used as an important index for GY estimation. [PERSON] et al. [24] used remote sensing inversion data on the LAI as the input parameter of an output estimation model to improve the accuracy of the output estimation. Based on this, an important objective of this study is to use the winter wheat canopy structure parameter LAI coupled with vegetation indices as input features for yield prediction, in order to effectively overcome the canopy spectral saturation phenomenon. The level of GY mainly depends on the number of ears, grains, and thousand seed weight of the crop [25]. During the growth stage of wheat from the jointing stage to the flag-raising stage, the LAI increases quickly, which is more conducive to accelerating the photosynthesis of plants. This period plays a decisive role in the number of ears of wheat. The number of grains in wheat mainly depends on the intensity of the photosynthesis from the flagging stage to the anthesis stage.