Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics

摘要： BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in ?eld crops is of great signi?cance for site-speci?c N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under ?eld conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with di?erent N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares–support vector machines (LS-SVM) were used for the classi?cation of di?erent N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classi?cation rates of 82% and 92% in prediction sets for the diagnosis of di?erent N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coe?cients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coe?cients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an e?ective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants.