CAJ | 학술논문

氮素是果树生长发育的一种大量必需元素，及时准确地监控果树的氮营养状况，对果树的合理施肥、增产、优化果实品质以及减缓过量施氮引起的水资源污染具有重要意义。利用高光谱成像技术结合多变量统计学方法，建立了柑橘植株叶片的含氮量预测模型。研究步骤为：高光谱扫描、提取平均光谱曲线、预处理原始光谱数据、采用连续投影法提取特征波段和建立含氮量预测模型。从SG平滑、SNV、MSC、1-Der等11种预处理方法中筛选出的较优预处理方法是SG平滑、Detrending和SG平滑-Detrending。对应这三种最优预处理方法，先采用连续投影法挑选出各自的特征波长，然后将各特征波段下的光谱反射率作为偏最小二乘、多元线性回归和反向传播人工神经网络模型的输入，各自建立三个预测模型。从以上获得的9个预测模型中，得出两个最优模型SG平滑-Detrending-SPA-BPNN（Rp ：0.8513，RMSEP ：0.1881）和Detrend-ing-SPA-BPNN（Rp ：0.8609，RMSEP ：0.1595）。结果表明，利用高光谱数据测定柑橘叶片含氮量具有可行性。这为实时、准确地监控柑橘植株生长过程中叶片含氮量的变化以及合理科学的氮肥施加提供了一定的理论基础。
담소시과수생장발육적일충대량필수원소，급시준학지감공과수적담영양상황，대과수적합리시비、증산、우화과실품질이급감완과량시담인기적수자원오염구유중요의의。이용고광보성상기술결합다변량통계학방법，건립료감귤식주협편적함담량예측모형。연구보취위：고광보소묘、제취평균광보곡선、예처리원시광보수거、채용련속투영법제취특정파단화건립함담량예측모형。종SG평활、SNV、MSC、1-Der등11충예처리방법중사선출적교우예처리방법시SG평활、Detrending화SG평활-Detrending。대응저삼충최우예처리방법，선채용련속투영법도선출각자적특정파장，연후장각특정파단하적광보반사솔작위편최소이승、다원선성회귀화반향전파인공신경망락모형적수입，각자건립삼개예측모형。종이상획득적9개예측모형중，득출량개최우모형SG평활-Detrending-SPA-BPNN（Rp ：0.8513，RMSEP ：0.1881）화Detrend-ing-SPA-BPNN（Rp ：0.8609，RMSEP ：0.1595）。결과표명，이용고광보수거측정감귤협편함담량구유가행성。저위실시、준학지감공감귤식주생장과정중협편함담량적변화이급합이과학적담비시가제공료일정적이론기출。
The present study presents prediction models for determining the N content in citrus leaves by using hyperspectral im-aging technology combined with several chemometrics methods .The steps followed in this study are :hyperspectral image scan-ning ,extracting average spectra curves ,pretreatment of raw spectra data ,extracting characteristic wavelengths with successive projection algorithm and developing prediction models for determining N content in citrus leaves .The authors obtained three op-timal pretreatment methods through comparing eleven different pretreatment methods including Savitzky-Golay(SG)smoothing , standard normal variate(SNV) ,multiplicative scatter correction(MSC) ,first derivative(1-Der) and so on .These selected pre-treatment methods are SG smoothing ,detrending and SG smoothing-detrending .Based on these three pretreatment methods ,the authros first extracted the characteristic wavelengths respectively with successive projection algorithm ,and then used the spec-tral reflectance of the extracted characteristic wavelengths as input variables of partial least squares regression (PLS) ,multiple linear regression (MLR) and back propagation neural network (BPNN) modeling .Hence ,the authors developed three predic-tion models with each pretreatment method ,and obtained nine models in total .Among all the nine prediction models ,the two models based on the methods of SG smoothing-detrending-SPA-BPNN (Rp :0.851 3 ,RMSEP:0.188 1)and detrending-SPA-BPNN (Rp :0.860 9 ,RMSEP :0.159 5)were found to have achieved the best prediction results .The final results show that u-sing hyperspectra data to determine N content in citrus leaves is feasible .This would provide a theoretical basis for real-time and accurate monitoring of N content in citrus leaves as well as rational N fertilizer application during the plant's growth .