Simultaneous inversion method of nitrogen and phosphorus contents in rice leaves using CARS-RUN-ELM algorithm

Abstract: Chemical fertilizers can be the zero and negative growth for the high requirement from the environmental and green development in recent years. Precise fertilization on demand can depend mainly on the rapid and accurate detection of crop nutrition and health status in fields. Among them, a synergistic inversion of nitrogen and phosphorus content can be expected to more comprehensively express the nutritional conditions of rice, compared with the single element inversion. It is also of great significance to the rice field management and accurate fertilization at the greening and tillering stage. In this study, a series of field plot experiments were conducted to realize the different treatments of nitrogen fertilizer. A chemical experiment was selected to obtain the nitrogen and phosphorus content in rice leaves, while a marine optical fiber spectrometer was used for the hyperspectral data of rice leaves. The data sets of nitrogen content were then sorted after measurement. A Kolmogorov-Smirnov test was also utilized to randomly divide the data sets into the 224 training and 93 verification sets, according to the ratio of 7:3. Competitive Adaptive Reweighted Sampling (CARS) was then used to screen the common characteristic wavelengths of nitrogen and phosphorus from the data sets. As such, the reflectivity of characteristic wavelengths was set as the input, whereas, the measured contents of nitrogen and phosphorus in the rice leaves were used as the output. A Back Propagation (BP) neural network, Extreme Learning Machine (ELM), and Runge-Kutta optimizer-Extreme Learning Machine (RUN-ELM) were used to construct the inversion models of nitrogen and phosphorus contents in the rice leaves. The results show that the CARS effectively removed a large number of redundant information in the hyperspectra data, where five common characteristic wavelengths of nitrogen and phosphorus were obtained to remove the collinearity characteristic wavelengths. After that, the characteristic wavelengths were selected as 451, 488, 780, and 813 nm. The best performance of the RUN-ELM model was achieved to retrieve the nitrogen and phosphorus contents in the rice leaves using the selected reflectance of characteristic wavelength. The determination coefficient and Root Mean Square Error (RMSE) of the nitrogen training set were 0.690 and 0.669 mg/g, respectively, while the determination coefficient and RMSE of the phosphorus training set were 0.620 and 0.027 mg/g, respectively. By contrast, the RUN-ELM model was superior to the BP neural network and ELM model in the prediction and simulation. Furthermore, the higher accuracy and stability of the ELM model were realized to improve the better weight and threshold, where the local optimal solution was avoided for the higher convergence speed than before. The reason was that the promising region was searched in the space using the calculated slope as the search logic and the Enhanced Solution Quality (ESQ) mechanism, according to the calculating gradient during Runge-Kutta (RK) optimization. To sum up, the CARS-RUN-ELM inversion model can rapidly and accurately extract the nitrogen and phosphorus content in the rice leaves. The high accuracy and stability of the model can greatly contribute to effectively gaining the nutrient element contents of rice leaves. The finding can provide a strong reference to detect the nitrogen and phosphorus content for the precise fertilization of rice on demand.