Combining the critical nitrogen concentration and machine learning algorithms to estimate nitrogen deficiency in rice from UAV hyperspectral data

Rapid and large area acquisition of nitrogen (N) deficiency status is important for achieving the optimal fertilization of rice. Most existing studies, however, focus on the use of unmanned aerial vehicle (UAV) remote sensing to diagnose N nutrition in rice, while there are fewer studies on the quantitative description of the degree of N deficiency in rice, and the effects of the critical N concentration on the spectral changes in rice have rarely been explored. Therefore, based on the canopy spectral data obtained by remotely-sensed UAV hyperspectral images, the N content in rice was obtained through field sampling. The construction method of the rice curve for the northeastern critical N concentration was studied, and on this basis, N deficiency was determined. Taking the spectrum of the critical N concentration state as the standard spectrum, the spectral reflectivity data were transformed by the ratios and differences, and the feature extraction of the spectral data was carried out by the successive projections algorithm (SPA). Finally, by taking the characteristic band as the input variable and N deficiency as the output variable, a set of multivariate linear regression (MLR), long short-term memory (LSTM) inversion models based on extreme learning machine (ELM), and the non-dominated sorting genetic algorithm III extreme learning machine (NSGA-III-ELM) were constructed. The results showed two key aspects of this system: 1) The correlation between the N deficiency data and original spectrum was poor, but the correlation between the N deficiency data and N deficiency could be improved by a difference change and ratio transformation; 2) The inversion results based on the ratio spectrum and NSGA-III-ELM algorithm were the best, as the R2 values of the training set and validation set were 0.852 and 0.810, and the root mean square error (RMSE) values were 0.291 and 0.308, respectively. From the perspective of the spectral data, the inversion accuracy of the ratio spectrum was better than the accuracy of the original spectrum or difference spectrum. At the algorithm level, the model inversion results based on LSTM algorithms showed a serious overfitting phenomenon and poor inversion effect. The inversion accuracy based on the NSGA-III-ELM algorithm was better than the accuracy of the MLR algorithm or the ELM algorithm. Therefore, the inversion model based on the ratio spectrum and NSGA-III-ELM algorithm could effectively invert the N deficiency in rice and provide critical technical support for accurate topdressing based on the N status in the rice.