Development and performance test of a vehicle-mounted total nitrogen content prediction system based on the fusion of near-infrared spectroscopy and image information

Abstract

To measure the total nitrogen (TN) content of large-scale farmland soil accurately in real time and guide field fertilization, a vehicle-mounted soil TN content prediction system based on the fusion of near-infrared spectroscopy and image information was developed. A combined algorithm of uniform variable illumination (UVE) and adaptive weighted sampling (CARS) were used to select the characteristic wavelength, and the selected characteristic wavelength was used as the soil spectral information. Multiple linear regression model, partial least squares regression model, BP neural network prediction model and Catboost prediction model were chosen to predict soil TN content. After comparison, Catboost was finally selected as the final prediction model. The prediction system was composed of a mechanical part, an optical part, and a control part. The mechanical part provided space support for the prediction system, the optical part obtained the spectrum and image information of the soil, mainly including seven single-band near-infrared (NIR) filters: 945, 1045, 1200, 1300, 1450, 1535, and 1615 nm; 1450 nm is the sensitive wavelength which is used to eliminate the effect of water. The control part realized spectral data collection, image feature processing, data fusion, and the prediction of soil TN content. Soil samples were used to test the accuracy of the spectrum collection of the system. The test results showed that the highest correlation coefficient between the reflectance of the prediction system at seven sensitive wavelengths and the reflectance of the NIRQuest 512 infrared spectrometer is above 0.943. The field test results show that the R2 between the predicted soil TN content by the prediction system and the value of laboratory standard detection method is greater than 0.80 and the relative errors are less than 10%. The results also show that data fusion of spectral data and image features highlighted the common contribution of the near infrared spectrum and visible light images in the detection of soil TN content, and it can improve the prediction accuracy of the instrument. Moreover, the system can provide guidance for farmland scientific variable fertilization.