摘要
冬小麦叶绿素含量的准确预测,可为冬小麦田间精细化管理提供依据。采集冬小麦冠层400~900 nm范围反射光谱,经一阶微分预处理后,为了抑制由于连续波长自变量多重共线性对叶绿素含量诊断模型的干扰,利用Gram-Schmidt正交变换算法初步提取叶绿素敏感波长特征参数为848、620、677 nm。在定量模型的建立过程中,对比了传统随机样本集划分与以空间中样本间距离远近为指导的SPXY样本集划分方法,并讨论了大田冠层反射光谱对叶绿素浓度诊断的最优精度,研究结果表明,以620 nm和677 nm两个敏感波长结合SPXY样本划分方法建立的多元线性回归模型预测精度较高,且叶绿素质量浓度为0.3 mg/L分辨间隔时,建模决定系数和验证决定系数分别达0.730和0.739,可为无损检测冬小麦拔节期叶绿素含量提供技术支持。
Accurate prediction of wheat chlorophyll content is important for guiding precision management in the field.The canopy spectrum of winter wheat canopy was measured by ASD Field Spec Handheld 2,and the first-order differential processing method was conducted on the band of 400 ~ 900 nm in the paper.In order to select the sensitive bands for the chlorophyll content detection of winter wheat,the Gram-Schmidt transformation algorithm was used in the research.The insignificant variables and the redundant information were identified and removed from the independent variables set.As a result,the orthogonal transformation data of first-order differential at 848 nm,620 nm and 677 nm were extracted.A representative set of wheat chlorophyll content of modeling samples was selected by using sample set partitioning based on joint x-y distance algorithm(SPXY).The results showed that multiple linear regression(MLR) prediction model based on Gram-Schmidt and SPXY algorithm is better than the random sampling method.The chlorophyll content of winter wheat were clustered respectively at intervals of 0.2 mg/L,0.3 mg/L and 0.5 mg/L.The modeling results showed that the optimal resolution was at0.3 mg/L,the determination coefficient R2 cand the R2 vof the calibration model which was built based on620 nm and 677 nm sensitive bands were respectively 0.730 and 0.739.The study could help to evaluate the nutritional status of winter wheat and precision fertilization.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2017年第S1期160-165,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2016YFD0300600-2016YFD0300606
2016YFD0300610)
国家自然科学基金项目(31501219)
中央高校基本科研业务费专项资金项目(2017TC029)
