Spectral inversion model of the crushing rate of soybean under mechanized harvesting

Abstract

Rapid and timely acquisition of the crushing rate can help in assessing the performance of combine harvesters, which is very important for agricultural production. The spectral reflectance of soybean provides an alternative method to the classical physical and chemical analysis of the crushing rate of soybean in laboratory. Therefore, hyperspectral imaging can be used to rapidly obtain the crushing rate of soybean. In this study, the hyperspectral method was employed, and the application of inter-correlation analysis was explored in the optimization and quantitative analysis of hyperspectral bands. The crushing rate of 130 soybean samples collected from a combine harvester was investigated through physical analysis in the laboratory. Subsequently, the raw hyperspectral reflectance of soybean samples was measured using a spectroradiometer equipped with a high intensity contact probe under darkroom conditions. Next, the raw spectral reflectance (REF) and the logarithmic reciprocal pretreatment spectrum data (LR) were analyzed and compared. The effective wavelengths were selected according to the results of the inter-correlation analysis. Regression models of the crushing rate with different indices were established using a least squares support vector machine (LS-SVM). The inversion results of the model were validated and compared with each other. The experimental results show that sensitive bands from REF are 1061, 1068, 1074, 1090, 2085, 2092, 2095, and 2103 nm. Sensitive bands from LR are 677, 1039, 1078, 1093, 1101, 1956, 2088, and 2107 nm. The results showed that REF was the optimal spectral index in the LS-SVM regression model (R_c² was 0.939, and R_p² was 0.915). The inter-correlation analysis method could not only support efficient selection of hyperspectral bands, but also retain the original sample information. The REF hyperspectral inversion model based on LS-SVM can realize rapid on-line monitoring of the performance (crushing rate) of grain combine harvesters in the future.

Keywords:
soybean; crushing rate; mechanized operations; least squares support vector machine; hyperspectral remote sensing