农业工程学报
農業工程學報
농업공정학보
2013年
8期
171-178
,共8页
张瑶%郑立华*%李民赞%邓小蕾%王诗丛%张锋%冀荣华
張瑤%鄭立華*%李民讚%鄧小蕾%王詩叢%張鋒%冀榮華
장요%정립화*%리민찬%산소뢰%왕시총%장봉%기영화
氮素%叶绿素%光谱分析%苹果树叶片%精细农业
氮素%葉綠素%光譜分析%蘋果樹葉片%精細農業
담소%협록소%광보분석%평과수협편%정세농업
nitrogen%chlorophyll%spectroscopy%apple tree leaves%precision agriculture
该文旨在利用光谱分析技术建立高精度苹果叶片营养素预测模型,为苹果树的精细管理提供技术支持.在苹果树年度生长周期的坐果期、生理落果期和果实成熟期等重要物候期,采集了180个果树叶片样本并测量了果树叶片在可见光和近红外波段的反射光谱,同时在实验室采用化学方法获取了果树叶片的氮素以及叶绿素含量.对于聚类后样本,分别分析了果树叶片反射光谱以及经小波滤波后的反射光谱与叶绿素以及氮素之间的相关关系,而后利用偏最小二乘和支持向量机(SVM,support vector machine)方法分别建立了果树叶片叶绿素和氮素含量的回归模型.研究发现,随着生长阶段的推进,在可见光处的反射率逐渐升高,在近红外处的反射率逐渐降低,且基于小波滤波反射光谱的营养素SVM回归模型精度最高:建立的叶绿素回归模型,其测定系数R2达到0.9920,均方根误差 RMSE为0.0039,验证精度R2达到0.9036,RMSE为0.1979;建立的氮素回归模型,其测定R2和验证R2也达到0.74以上,模型的回归RMSE为0.0554,验证RMSE为0.1215.结果表明,采用支持向量机回归模型可以精确估计果树叶片叶绿素含量,对氮素含量的估计精度也达到了实用化水平.
該文旨在利用光譜分析技術建立高精度蘋果葉片營養素預測模型,為蘋果樹的精細管理提供技術支持.在蘋果樹年度生長週期的坐果期、生理落果期和果實成熟期等重要物候期,採集瞭180箇果樹葉片樣本併測量瞭果樹葉片在可見光和近紅外波段的反射光譜,同時在實驗室採用化學方法穫取瞭果樹葉片的氮素以及葉綠素含量.對于聚類後樣本,分彆分析瞭果樹葉片反射光譜以及經小波濾波後的反射光譜與葉綠素以及氮素之間的相關關繫,而後利用偏最小二乘和支持嚮量機(SVM,support vector machine)方法分彆建立瞭果樹葉片葉綠素和氮素含量的迴歸模型.研究髮現,隨著生長階段的推進,在可見光處的反射率逐漸升高,在近紅外處的反射率逐漸降低,且基于小波濾波反射光譜的營養素SVM迴歸模型精度最高:建立的葉綠素迴歸模型,其測定繫數R2達到0.9920,均方根誤差 RMSE為0.0039,驗證精度R2達到0.9036,RMSE為0.1979;建立的氮素迴歸模型,其測定R2和驗證R2也達到0.74以上,模型的迴歸RMSE為0.0554,驗證RMSE為0.1215.結果錶明,採用支持嚮量機迴歸模型可以精確估計果樹葉片葉綠素含量,對氮素含量的估計精度也達到瞭實用化水平.
해문지재이용광보분석기술건립고정도평과협편영양소예측모형,위평과수적정세관리제공기술지지.재평과수년도생장주기적좌과기、생리낙과기화과실성숙기등중요물후기,채집료180개과수협편양본병측량료과수협편재가견광화근홍외파단적반사광보,동시재실험실채용화학방법획취료과수협편적담소이급협록소함량.대우취류후양본,분별분석료과수협편반사광보이급경소파려파후적반사광보여협록소이급담소지간적상관관계,이후이용편최소이승화지지향량궤(SVM,support vector machine)방법분별건립료과수협편협록소화담소함량적회귀모형.연구발현,수착생장계단적추진,재가견광처적반사솔축점승고,재근홍외처적반사솔축점강저,차기우소파려파반사광보적영양소SVM회귀모형정도최고:건립적협록소회귀모형,기측정계수R2체도0.9920,균방근오차 RMSE위0.0039,험증정도R2체도0.9036,RMSE위0.1979;건립적담소회귀모형,기측정R2화험증R2야체도0.74이상,모형적회귀RMSE위0.0554,험증RMSE위0.1215.결과표명,채용지지향량궤회귀모형가이정학고계과수협편협록소함량,대담소함량적고계정도야체도료실용화수평.
@@@@This research aimed at exploring the VIS/NIR (Visible Spectrum/Near Infra Red) reflectance spectral characteristics of apple tree leaves, and establishing a high-precision model to predict nutrient content for these leaves. Samples were collected from the apple orchard of Beijing Xiangtang culture village during the period of fruit-bearing, fruit-falling and fruit-maturing separately. The apple trees in the orchard were in the full productive age. Twenty apple trees (15 year-on trees and 5 year-off trees) were selected randomly from different regions. 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 the leaves from the same representative part were considered as one sample. In the end, 60 samples of apple leaves were collected in each phenological period, and the visible and near infrared spectral reflectance were measured using Shimadzu UV-2450 spectrograph. At the same time, the chlorophyll content for each sample was detected using spectrophotometry and the nitrogen content of each sample was measured using the Kjeldahl method in the laboratory. The obtained spectral reflectance and nutrient content were clustered based on each bough individually. The first through seventh layer wavelet decompositions were done to the original spectrum respectively. It can be perceived that with the decomposition scale increasing, the curve became smoother because of eliminating the impact of random noise, while some valid information was lost at the same time. According to the correlation analysis, this study selected 3-layer db4 wavelet filtering spectral information to predict the nitrogen and chlorophyll content. After that, correlation analyses were conducted between: 1) the chlorophyll content of apple tree leaves and their spectral reflectance; 2) the chlorophyll content of apple tree leaves and their spectral reflectance under wavelet filtering; 3) the nitrogen content of apple tree leaves and their spectral reflectance;and 4) the nitrogen content of apple tree leaves and their spectral reflectance under wavelet filtering. Then, the regression models for predicting nitrogen content and chlorophyll content of apple tree leaves were established using PLS (Partial Least Square) and SVM (Support Vector Machine) methods, respectively, based on the above spectral signal. The results indicated that:1) with the advance of growth stage, the reflectance at visible waveband increased gradually, while at the near infrared waveband, the reflectance decreased gradually; 2) wavelet analyzing technology could distinguish the mutation part and noise in the spectral signals effectively, which make it possible to retain the maximum amount of effective information during the signal denoising process. The wavelet filtering technology played a significant role in promoting the modeling accuracy in predicting the Chlorophyll;3) the models based on the SVM method had higher accuracies; 4) for the Chlorophyll regression model based on the spectral reflectance under wavelet filtering, the calibration R2 reached to 0.9841, RMSEC was 0.0039, and the validation R2 of reached to 0.9036, RMSEP was 0.0567;and 5) for the nitrogen regression model, the R2 of calibration and validation model were all above 0.74, RMSEC was 0.0554 and RMSEP was 0.1215. It was concluded that the chlorophyll SVM regression model reached a high accuracy, and the nitrogen SVM regression model also reached the practical level with high stability.
