Gram-Schmidt算法在加工番茄病叶水分估测中的应用  

Water Content Estimation in Processing Tomato Leaves Using Gram-Schmidt Algorithm

作　　者：尹小君[1,2] 李满春[1] 赵思峰[3] 王登伟[3] 

机构地区：[1]南京大学地理信息科学系,江苏南京210093 [2]石河子大学信息科学与技术学院,新疆石河子832000 [3]石河子大学农学院,新疆石河子832000

出　　处：《水土保持通报》2012年第2期132-136,153,共6页Bulletin of Soil and Water Conservation

基　　金：国家"十一五"科技支撑计划项目"干旱半干旱区土壤农药污染控制关键技术研究与示范"(2007BAC20B04);国家自然科学基金项目(30800733)

摘　　要：Gram—Schmidt正交变换算法通过成分提取提高了非线性回归模型的精度。利用Gram—Schmidt正交变换算法,对加工番茄病叶相对水分含量进行了高光谱估测。研究结果表明,病叶相对水分含量的敏感波段为可见光波段R695以及近红外波段R761,R1446,R1940和R2490。对敏感波段进行正交变换,得到相对水分含量与R1940和R2490的非线性回归模型,相关系数为0.724,相对误差为0.52%,标准误差(RMSE)为0.13,真实值与预测值拟合的相关系数为0.623,表明该方法优于传统相对水分含量的线性模型。研究结果可为加工番茄病害胁迫下品质的精确诊断提供了技术支撑。Based on measured water contents and spectral reflectance of the bacterial spots on tomato leaves,we attempted to estimate the water contents of diseased leaves using the Gram—Schmidt transformation algorithm.The results show R694 in visible and R761,R1446,R1940,and R2490 in near-infrared wavelengths were the spectra sensitive to the variations of water contents.Non-linear regression models were then developed to predict water contents using reflectance at R1940 and R2490 using Gram—Schmidt orthogonal transformation algorithm,with high R2（0.724）,low relative error（0.52%） and RMSE（0.13）.This model was proved superior to the traditional linear model.The findings of this research can provide technical supports for diseases diagnosis of tomato plants under stress.

关 键 词：Gram—Schmidt 加工番茄 病叶 水分含量 估测 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] O212.3[自动化与计算机技术—控制科学与工程]