The research area is located in Qixia City, Yantai, China, which is known as the “Apple City of China” with the east longitude of 120˚33' - 121˚15' and northern latitude of 37˚05' - 37˚32'. The orchard area reaches 4.3 × 10⁴ ha and the main planting variety is Red Fuji apple tree. The temperate zone monsoon subhumid climate brings four distinctive seasons, abundant sunlight, 11.3˚C average annual temperature, 754 mm annual precipitation, 2690 h total sunshine hours and 207 d annual frost-free period. The terrain is mainly low mountains and hills and agrotype is mainly brown soil [15] .

The test was carried out on September 22, 2014 in Qixia City and the tested apple cultivar is Red Fuji apple. Depending upon the distribution characteristics of orchards, 105 apple trees of 32 orchards in 14 towns were selected as research objects. The determining instrument is Field Spec 4 portable field spectrometer produced by ASD Inc. Wave band scope of this instrument is 350 - 2500 nm, of which, the sampling interval of 350 - 1000 nm is 1.4 nm and spectral resolution is 3 nm; the sampling interval of 1000 - 2500 nm is 2 nm and the spectrum resolution is 8 nm. When the weather was sunny, no-cloud, no-wind or of breeze, the reflectivity of canopy was measured at 10:00 - 14:00. A 5 m external optical fiber was adopted in the measurement. The optical fiber field angle was 25˚ and the probe faced downward vertically. The selection of appropriate determination height was based on the size of the canopy so as to guarantee the whole canopy was in the field of view. Each sample was measured for 10 times and their average value was taken as the value of hyperspectral reflectivity. Standard white plate correction was required every 15 minutes to ensure the measurement accuracy [2] .

Twelve leaves were collected at each canopy and putted into freshness protection package when measuring. These leaves could be kept in cold and dark place and brought back to laboratory. De-enzyme was conducted for 20 minutes at the dryer with the temperature of 80˚C, then the drying process with 60˚C was conducted until constant weight was achieved. The dried sample was grinded into powder with mortar and selected by a screen of 0.25 mm. The nitrogen content was determined using the Kjeldahl method.

The spectrum showed obvious fluctuation at 1350 nm, 1850 nm and 2500 nm due to the influence of the instrument itself and external environment, especially the moisture in atmosphere. Previous research has found that wave bands highly correlated with nitrogen content are centralized at visible light area. Therefore, the apple tree canopy spectrum of 350 - 1000 nm was selected as the research spectral scope based on the wave band characteristic of Field Spec 4 spectrometer to eliminate the interference of external factors such as moisture, etc.

To verify that MSC has effect on the improvement of nitrogen spectral quantitative inversion accuracy, the original spectral reflectivity went through first derivative and multiple scattering correction plus first derivative respectively. The MSC algorithm is as following:

Calculate the average spectrum:

Conduct linear regression on spectrum and average spectrum of each sample:

Calculate the corrected spectrum:

Of which, i = 1, 2, …, n and n is the number of samples; j means the No. j of wavelength.

The correlation coefficients were calculated between each wavelength and nitrogen content. The sensitive wavelengths were selected via testing significance to establish the multiple nonlinear regression models through SVM. The effect of MSC was evaluated through analyzing the correlation coefficient (r) between spectral data after different pre-processing and nitrogen content and studying the determination coefficient (R²) and root mean square error (RMSE) of the model. The model with the highest R² and lowest RMSE was considered the best.

The original reflectance spectral curve was obtained at 350 - 1000 nm (Figure 1). It can be found that the absolute intensity greatly differed from one another and obvious baseline translation existed at the spectral interval. This was mainly influenced by different canopy structure, light condition, etc. The curve of spectral reflectance characteristics after MSC processing was as shown in Figure 2. The absolute intensity difference reduced significantly, the spectrum was more centralized and the spectral reflection characteristic was more obvious at the same time.

The correlation coefficient at each wavelength between the nitrogen content and the raw spectral value and MSC spectral reflectivity, was calculated, respectively (Figure 3). It can be seen from Figure 3 that the correlation coefficients were improved significantly after MSC. At 660 nm - 683 nm, MSC spectra showed significant correlation

with apple tree canopy nitrogen content (P < 0.01).

To further improve the correlation and easier select the sensitive waveband, first derivative processing was applied to the original spectrum and MSC spectrum respectively. As was shown in Figure 4 and Figure 5, the correlation coefficient between FD spectrum and MSC + FD spectrum and nitrogen content showed significant improvement. Meanwhile, the FD correlation coefficients were higher than the MSC + FD correlation coefficients. Of which, 7 wavelengths in FD correlation coefficients reached significant correlation (Figure 4); 12 wavelengths in correlation coefficients in MSC + FD correlation coefficients reached significant correlation (Figure 5).

Table 1 showed the wavelengths with significant correlation between FD spectrum and MSC + FD spectrum and apple tree canopy nitrogen content. It can be seen from

Table 1 showed that the FD 7 wavelengths of significant correlation all showed the same in MSC + FD. Moreover, the correlation coefficients of the FD 7 wavelengths were higher than MSC + FD.

To compare the prediction accuracy, the same 7 wavelengths were taken as independent variables and apple tree canopy nitrogen contents were taken as dependent variables to the prediction model through SVM method. In the 105 samples, 70 were randomly selected as calibration set and 35 were as prediction set. Samples in calibration set conformed to normal distribution.

SVM parameters were determined after repeated training: penalty coefficient was 1

**P < 0.01.

and kernel function parameter was 0.6. The modeling and prediction results were as shown in Table 2. The with MSC + FD processing compared with FD has risen to 0.746 from 0.624, and has risen from 0.535 to 0.720. The RMSE_v has declined from 0.936 g・kg⁻¹ to 0.756 g・kg⁻¹. Therefore, the model based on MSC + FD processing can predict apple tree canopy nitrogen content in a better manner. The MSC processing can improve the accuracy of prediction model.