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Hyper-spectral remote sensing technology was used to explore the estimation method of nitrogen content in the leaves of macadamia to achieve a rapid diagnosis of nitrogen nutrition in macadamia trees. Lincang and Xishuangbanna were chosen as the research area to obtain the spectral reflectance and nitrogen content of the leaves of macadamia varieties O.C and HAES344. Firstly, multiple mathematical transformations were performed on the original spectral reflectance using logarithmic transformation, derivative transformation, and their combinations. Then, the correlation between nitrogen content of macadamia leaves and spectral data of different transformation forms was analyzed. Under the principle of larger determination coefficient, the wavelength corresponding to the peak characteristic point in the determination coefficient curve was selected as the nitrogen sensitive wavelength, thus the corresponding spectral variables of nitrogen sensitivity were obtained. Stepwise regression was used to further optimize the nitrogen sensitive spectral variables, and the methods of multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR) were used to construct the nitrogen content estimation models for macadamia leaves. Finally, the performance of the models was tested using validation and test sets, respectively. The results showed that the MLR, PLSR and SVR models all performed well in estimation, and the ratio of performance to standard deviate (RPD) of both the validation and test sets were above 2.0. Among them, the PLSR was the optimal estimation model, its RPD of the validation set and the test set was 2.099 and 2.110, respectively. The 19 nitrogen sensitive spectral variables selected from 6 types of transformation spectral data, including reflectance (R), logarithmic transformation of reflectance (LR), first derivative of reflectance (FDR), first derivative of logarithmic transformation of reflectance (FDLR), second derivative of reflectance (SDR), and second derivative of logarithmic transformation of reflectance (SDLR) had strong stability in nitrogen spectral response. Based on the selected 19 nitrogen sensitive spectral variables, the conventional regression modeling methods could achieve good estimation results and had strong regional universality. In this study, nitrogen sensitive spectral variables were selected from a variety of transform spectral data, which provided a new idea for the nitrogen content estimation of macadamia leaves.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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