Application of information fusion technology in maize fertilizer utilization experiments

Abstract

Abstract The article first utilizes the hyperspectral image grayscale, texture information, and reflectance spectral information at characteristic wavelengths to establish the corresponding quantitative analysis models of nutrient content in maize plants using three modeling methods, MLR, PCR, and PLS. Then from the basis of polarized reflection features, it was inferred that the polarization degree features at sensitive wavelengths were extracted using polarized spectra and combined with chemometric techniques to achieve a quantitative analysis of the degree of nutrient stress in maize. Finally, the feature variables extracted on the hyperspectral and polarized-reflection spectral measurement systems were fused with multiple information. A diagnostic evaluation model of fertilizer utilization with polarization-hyperspectral multidimensional light information was established. The results showed that for the new slow-release fertilizers, SF1 and YNPK had higher nitrogen utilization rates, 9.91% and 7.43% higher than N1PK, respectively. And the nitrogen fertilizer utilization rate was correspondingly higher by 6%-7% in 2020 than in 2019 for each fertilizer application treatment.