Monitoring the nitrogen nutrition status of rice plants using spectral and image technologies

Abstract

Background: We aimed to investigate methods to estimate the nitrogen (N) nutrition status of rice plants using data obtained using a digital camera and a spectroradiometer. The overall aim was to compare the advantages and potential of image technology and spectral technology to monitor rice N indexes accurately, inexpensively, and in real time to optimize fertilization strategies. Realizing the technical selection of definite spectrum or image diagnosis aiming at different rice nitrogen nutrition indexes. We conducted field trials of rice plants grown with different levels of N fertilizer in 2018 to 2019. Spectral information and images of the rice canopy were obtained, various image and spectral characteristic parameters were selected to construct models to estimate rice N status.Results: The determination coefficients of the models constructed using the ratio vegetation index (RVI[800,550]) and cover canopy (CC) as dependent variables were most significant. Among the models using spectral parameters, those constructed using RVI[800,550] to estimate rice N indexes had the obviously coefficient of determination (R2) values, which were 0.69, 0.58, and 0.65 for the models to estimate leaf area index(LAI), aboveground biomass(AGB), and plant N accumulation(PNA). As for image parameter, those using CC to predict rice N indexes showed the highest R2 values (0.76, 0.65, and 0.71 for the models to estimate LAI, AGB, and PNA, respectively) (P < 0.01). The model using the spectral parameter RVI[800,550] had a good fit and stability in estimating plant nitrogen accumulation (R2 = 0.65, root mean square error (RMSE) = 1.35 g·m-2, relative RMSE (RRMSE) = 14.05%), and the model using the image parameter CC had a good fit in predicting leaf area index (R2 = 0.76, RMSE = 0.28, RRMSE = 7.26%) and aboveground biomass (R2 = 0.65, RMSE = 22.03 g·m-2, RRMSE = 7.52%). Different detection technology should be adopted for different rice varieties and rice N nutrition indexes. Conclusions: Spectral and image parameters can be used as technical parameters to estimate rice N status. The spectral parameter RVI[800,550] can be used to accurately estimate plant nitrogen accumulation, and the image parameter CC can be used to accurately estimate leaf area index and aboveground biomass.