Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

Abstract

The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring

“…Numerous studies have shown that the pre-CRP method is only suitable for short periods on sunny days, but there have been few studies of the accuracy of correction methods during complex weather conditions [7]. The post-image enhancement radiometric correction method uses complex processing and has low precision; a simple and effective correction method that could improve image quality is highly desirable [16].…”

“…The pre-calibration reference panel (pre-CRP) is a standard radiometric correction method under sunny conditions that is used for studying and resolving variations in reflection spectra caused by environmental changes in the collected multispectral or hyperspectral data [6,7]. The calibration reference panel (CRP) images captured before and after flight missions are used to establish a relationship between reflectance and the corresponding values collected by a UAV-mounted sensor.…”

Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review

“…There are some methods to obtain the most relevant sensitive bands from high-dimensional data samples. For instance, the combination of principal component analysis (PCA) with a genetic algorithm [106], the partial least squares regression (PLSR) [107], and the Gaussian process regression (GPR) [108] can reduce the dimensionality of the original data, thereby decreasing redundant information in the data and obviously increasing the data validity.…”

Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

“…Currently, the methods for reducing the dimension of extracted spectral data from hyperspectral images mainly include feature extraction based on transformation (Du et al, 2018) [e.g., principal component analysis (PCA)] and feature selection based on non-transformation (Salimi et al, 2018;e.g., algorithms for selecting local feature bands). Peerbhay et al (2015) used hyperspectral remote sensing for the detection and mapping of Solanum mauritianum located within commercial forestry ecosystems.…”