CAJ | 학술논문

以苹果叶片叶绿素含量为研究对象，定量研究了光谱数据预处理方法对光谱特征提取及叶绿素含量预测模型的影响。首先，比较了苹果叶片原始反射率光谱、小波包去噪反射率光谱、反射率一阶差分光谱、先小波包去噪后一阶差分光谱、先一阶差分后小波包去噪光谱这5种光谱的波段间相关系数以及光谱与叶绿素含量间的相关系数，建立了叶绿素含量预测逐步回归模型并对建模结果进行了比较分析。结果表明单纯3层sym8小波包去噪可使光谱曲线平滑，但不会明显提高模型精度；一阶差分虽然放大了局部噪声，但是消除了基线漂移影响，可提高模型精度；先差分后小波包去噪比先小波包去噪后差分具有更高的峰值信号噪声比，更低的均方误差与最大误差，建模结果也显示出同样的结果。因此，先差分后小波包去噪算法可认为是一种有效的苹果叶片叶绿素含量预测光谱预处理方法。利用这一方法建立了苹果叶片叶绿素含量预测模型，获得了较高的预测精度。该研究可用于对苹果树营养状态的评价并指导按需施肥。
이평과협편협록소함량위연구대상，정량연구료광보수거예처리방법대광보특정제취급협록소함량예측모형적영향。수선，비교료평과협편원시반사솔광보、소파포거조반사솔광보、반사솔일계차분광보、선소파포거조후일계차분광보、선일계차분후소파포거조광보저5충광보적파단간상관계수이급광보여협록소함량간적상관계수，건립료협록소함량예측축보회귀모형병대건모결과진행료비교분석。결과표명단순3층sym8소파포거조가사광보곡선평활，단불회명현제고모형정도；일계차분수연방대료국부조성，단시소제료기선표이영향，가제고모형정도；선차분후소파포거조비선소파포거조후차분구유경고적봉치신호조성비，경저적균방오차여최대오차，건모결과야현시출동양적결과。인차，선차분후소파포거조산법가인위시일충유효적평과협편협록소함량예측광보예처리방법。이용저일방법건립료평과협편협록소함량예측모형，획득료교고적예측정도。해연구가용우대평과수영양상태적평개병지도안수시비。
Great progress has been achieved in the prediction of vegetation biological parameters based on spectroscopy, and most studies were focused on building models by the mathematical combination of the reflectance, the red edge and the blue edge, and different modeling methods, to improve the accuracy of the models. Different combinations of preprocessing methods can get different accuracies. However, it is not inevitable that all preprocessing methods can help to improve the accuracy, and the same combination of the methods for the same data with different steps may get different accuracies. Thus, in this paper, the impacts of the preprocessing methods and steps on the spectral feature extraction and the models are discussed. Derivative spectra can eliminate the effect of baseline drift, reduce background interference, and provide higher spectral resolution than the original spectra. Wavelet packet transform can decompose the low-frequency and high-frequency parts of the signal and thus, show obvious advantages in signal denoising. Therefore, these two preprocessing methods and the combinations with different steps were studied. Taking apple leaf chlorophyll content as the research object, spectral autocorrelation coefficients, correlation coefficients between spectral data and the chlorophyll content, and stepwise regression modeling were calculated for the reflectance spectra, including the original reflectance spectra, wavelet packet denoising reflectance spectra, first-order differential reflectance spectra, the first-order differential of the wavelet packet denoising reflectance spectra, and wavelet packet denoising of the first-order differential reflectance spectra. The 60 apple leaf samples were collected from the top, middle, and bottom positions of sunny main branches from 20 apple trees, and the reflectance and the chlorophyll content were then measured. The spectral data of the 60 apple leaf samples, ranging from 300 to 850 nm by different preprocessing methods, were formed into matrices (60×551), and the spectral autocorrelation coefficients were then calculated. The effects of the denoising methods were evaluated by peak signal-to-noise ratio (PSNR), lower mean square error (MSE) and maximum squared error (MAXERR). At the same time, the accuracies of the predicted models were evaluated by the r and root mean square error (RMSE). The spectral curve can be smoothed by the 3-layer sym8 wavelet packet de-noising, but the modeling accuracy was not improved. Therefore, it was not reliable in evaluating the effect of the denoising methods only by the naked eye. It was important to choose the proper parameters for wavelet packet denoising. Although the noise was amplified by the first-order differential, the baseline drift was removed and thus, the accuracy of the model was improved. The wavelet packet denoising of the first-order differential reflectance spectra had higher PSNR, lower MSE and MAXERR than the first-order differential of the wavelet packet denoising reflectance spectra. The r and RMSE of the regression models for these two methods were 0.746, 5.01 and 0.683, 5.44, respectively. The former method had higher r and lower RMSE. Therefore, the denoising of the first differential reflectance spectra had a better denoising effect and linear regression model accuracy than the first differential of the denoising reflectance spectra. Thus, wavelet packet denoising of the first-order differential reflectance spectra could be considered as an effective preprocessing method to improve modeling accuracy. The study can satisfy the demands of evaluating the nutritional status of apple tree and precision fertilization.