基于光谱学原理与小波包分解技术预测苹果树叶片氮素含量  

Predicting apple tree leaf nitrogen content based on hyperspectral and wavelet packet analysis

作　　者：张瑶[1] 郑立华[1] 李民赞[1] 邓小蕾[1] 

机构地区：[1]中国农业大学"现代精细农业系统集成研究"教育部重点实验室,北京100083

出　　处：《农业工程学报》2013年第A01期101-108,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31071330);863计划(2011AA100703;2011AA100704)联合资助

摘　　要：为探索不同生理物候期苹果树叶片氮素含量的快速检测方法。分别在果树坐果期、生理落果期和果实成熟期,使用光谱仪测量了果树叶片在可见光和近红外区域的反射光谱,同时在实验室测定了果树叶片的全氮含量。研究首先将实验所得的光谱反射率与氮素含量以果树为单位进行聚类,利用小波包分析技术对每棵果树的光谱信息进行分解,提取出的低频信号和去除高频噪音后的信号分别组成了低频全光谱和去噪全光谱。针对这两个全光谱均实施了主成分分析,利用提取主成分分别建立了果树不同生长阶段的氮素含量多元线性回归模型。对比基于归一化植被指数(NDVI)建立的氮素含量估测模型发现,利用全光谱信息建立的氮素含量预测模型精度更高;在坐果期和果实成熟期,使用去噪全光谱提取的主成分建立的氮素预测模型最优;而在生理落果期,使用低频全光谱提取的主成分建立的模型最优。结果表明,利用小波包分析技术能够有效地提高苹果果树叶片氮素含量的光谱预测能力。This research is aimed at exploring high accuracy method on detecting nitrogen content for apple leaves in different physiological phenological phases. The experiments were conducted during the periods of fruit-bearing, fruit-falling and fruit-maturing separately. 20 apple trees were selected randomly from different regions in an apple orchard located in Beijing suburb, China. Then a main branch of each target tree was selected and three representative parts （base part, middle part and top part） of every bough were marked. And then leaves samples were collected from each representative part of each target tree, and 60 leaves samples were obtained in each phenological period. The collected samples were carried to the laboratory quickly, and their visible and NIR spectral reflectance were measured using Shimadzu UV-2450 spectrograph and their nitrogen content were detected using Kjeldahl method. For data processing, firstly data cluster analysis was conducted among the spectral reflectance and nitrogen content based on individual tree, hence 20 new sample data were obtained accordingly. Then the spectrum of each tree was decomposed using wavelet packet technology. The results revealed that with the wavelet packet decomposition scale increasing, signal of spectrum low-frequency and de-noised high-frequency separated gradually. The low-frequency signal became smoother apparently, some peak-valleys reflecting the biological characteristics disappeared. For the de-noised high-frequency signal, it didn’t change significantly with decomposition scale deepened in the visible region, while the noise decreased in the near infrared region. And then principle component analysis was applied respectively to the original spectra, extracted low-frequency spectra and de-noised high-frequency spectra. Finally, linear regression models for predicting leaf nitrogen content were established based on the principle components extracted from the according spectra and NDVI （859 nm, 364 nm）. The results indicated that： （1）

关 键 词：氮素 主成分分析 光谱仪 苹果叶片 小波包分解 NDVI 多元线性回归 

分 类 号：O657.3[理学—分析化学] S126[理学—化学]