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, anthesis 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 R2 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 (R2 = 0.74), and the R2 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.
Highlights
Published: 20 December 2021Wheat is one of the most widely cultivated food crops in the world, with China ranking first in terms of yield and sales [1]
The results show that the coefficient of variation (CV) values of grain yield (GY) from both growing seasons separately were similar, while the CV of overall GY from both growing seasons had elevated compared to the results from single growing season only
The results showed that the p-values for all four growth stages were less than 0.05, indicating that the vegetation indices coupled with leaf area index (LAI) were used to construct GY prediction models with statistically significant improvement in prediction accuracy
Summary
Published: 20 December 2021Wheat is one of the most widely cultivated food crops in the world, with China ranking first in terms of yield and sales [1]. The level of GY is influenced by complex factors such as light, soil, moisture, and atmosphere, which poses a great challenge to the accurate prediction of GY [8]. Remote sensing technology has been successfully applied to crop growth monitoring through satellite platforms, manned airborne platforms, and ground spectral equipment [11]. Several limitations such as deficient temporal-spatial resolution and cloud cover contamination restrain the application of satellite-based platforms [12]. Near-ground hyperspectral remote sensing data is cost-effective, and has narrow band, strong continuity, Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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