摘要
利用0.06-200 k Hz范围内拥有36个频率点的LCR测量仪及自制夹持平行电极板,测量280片不同含水率玉米叶片的相对介电常数ε＇及介电损耗因子ε″;利用干燥法测量玉米叶片的湿基含水率。利用逐步回归法（SWR）与多元线性回归（MLR）结合的线性建模方法和连续投影算法（SPA）与支持向量回归（SVR）结合的非线性建模方法,分别建立玉米叶片介电参数（ε＇、ε″及两者融合信息3种参数）与湿基含水率的关系模型,并应用留一交叉验证法选取2种建模方法的最佳关系模型。分析表明,非线性模型较线性模型具有更高的预测能力,且基于ε＇与ε″的融合信息运用连续投影算法（SPA）与支持向量回归（SVR）相结合的非线性建模方法使模型原72个变量精简到10个,剔除了模型中冗余度较高的变量,有效降低了模型的复杂度,得到最高的测试集决定系数R2P（0.804）和最小的测试集均方根误差RMSEP（0.017 6）。结果表明基于介电特性的玉米叶片含水率无损检测方法是可行的,为快速检测其他农作物的生理信息提供了一种可靠的方法。
Moisture content is a major index in the healthy growth of crops. It is beneficial to water and fertilizer management when the crop moisture content is detected timely. The dielectric properties（ relative dielectric constant ε＇ and dielectric loss factor ε″） of 280 pieces of corn leaves with different moisture contents were measured with a self-made clamping capacitor and an LCR measuring instrument at 36 discrete frequencies over the frequency range of 0. 06 - 200 k Hz and the moisture content of the corn leaves were measured by drying weight method. To obtain the moisture content of corn leaves,linear regression methods（ the combination of SWR and MLR） and nonlinear regression methods（ SPA and SVR） were used to establish models to get the relationship between the moisture content and dielectric parameters（ ε＇,ε″ and the combination of ε＇ and ε″）,and the leave one out cross validation（ LOOCV）was used to select the best models. The results showed that contrasted with the linear regression method,the nonlinear regression method had better predictive ability. The highest coefficient of determination（ 0. 804） and the lowest root mean square error（ 0. 017 6） were obtained by using the nonlinearregression model with the variable in the combination of ε＇ and ε″,which simplified the model with variables reduced from 72 to 10 and eliminated the overlap variables,and the complexity of the model was decreased effectively. The study indicated that it was feasible to detect the corn leaf moisture content non-destructively,and the results provided a credible method for rapid non-destructive detection of physiology information in crops.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2016年第4期257-264,279,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(31471413)
江苏高校优势学科建设工程PAPD项目(苏政办发[2011]6号)
江苏大学现代农业装备与技术重点实验室开放基金项目(NZ201306)
江苏省自然科学基金项目(BK20141165)
农业部烟草生物学与加工重点实验室开放课题项目(20150001)
江苏省'六大人才高峰'项目(ZBZZ-019)
关键词
玉米叶片
湿基含水率
无损检测
介电特性
回归算法
corn leaves
moisture content
non-destructive detection
dielectric properties
regression algorithm
