CAJ | 학술논문

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.
위탐색불동생리물후기평과수협편담소함량적쾌속검측방법.분별재과수좌과기、생리낙과기화과실성숙기,사용광보의측량료과수협편재가견광화근홍외구역적반사광보,동시재실험실측정료과수협편적전담함량.연구수선장실험소득적광보반사솔여담소함량이과수위단위진행취류,이용소파포분석기술대매과과수적광보신식진행분해,제취출적저빈신호화거제고빈조음후적신호분별조성료저빈전광보화거조전광보.침대저량개전광보균실시료주성분분석,이용제취주성분분별건립료과수불동생장계단적담소함량다원선성회귀모형.대비기우귀일화식피지수(NDVI)건립적담소함량고측모형발현,이용전광보신식건립적담소함량예측모형정도경고;재좌과기화과실성숙기,사용거조전광보제취적주성분건립적담소예측모형최우;이재생리낙과기,사용저빈전광보제취적주성분건립적모형최우.결과표명,이용소파포분석기술능구유효지제고평과과수협편담소함량적광보예측능력.
@@@@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) in different psychological phonological phases, the total nitrogen content forecasting models built with different wavelet packet decomposition spectra had higher accuracy than that with NDVI since full spectra could reserve more valid information than the signals at two sensitive wavebands; (2) the models established using the principal components extracted from the de-noised high-frequency spectra had the highest accuracy in fruit-bearing and fruit-maturing period. While in physiological fruit-falling period, the model established by the principal components extracted from the low-frequency spectra was the best; (3) in fruit-bearing period, the highest accuracy regression model went to which established based on the principal components extracted from the high-frequency noise removed spectra after 5-layer decomposition. Its calibration R2 reached to 0.9502, RMSEC was 0.0978, and the validation R2 reached to 0.7285, RMSEP was 0.0885; (4) in fruit-falling period, the best regression model went to that established based on the principal components extracted from the low frequency spectra after 7-layer decomposition. Its calibration R2 reached to 0.9539, RMSEC was 0.0553, and the validation R2 reached to 0.9273, RMSEP was 0.087; (5) in fruit-maturing period, the best regression model was that established based on the principal components extracted from the high-frequency noise removed spectra after 3-layer decomposition. Its calibration R2 reached to 0.9577, RMSEC was 0.0576, and the validation R2 reached to 0.9013, RMSEP was 0.0791;(6) wavelet packet decomposition technique is an effective way to enhance the spectrum prediction ability of apple tree leaves nitrogen content, meanwhile in order to improve the predicting accuracy, wavelet packet decomposition level should be determined based on the spectral characteristics in different physiological phonological phases.