基于主成分分析和人工神经网络的稻穗健康状态的高光谱识别

Hyperspectral discrimination of different health conditions in rice panicles based on principal component analysis and artificial neural network

水稻生长后期穗部遭受病虫危害会严重影响水稻产量,对不同健康状态的稻穗进行精准识别是采取病虫害防控措施和危害评估的依据。研究测定了健康稻穗、轻度、中度和重度危害稻穗及白穗的室内高光谱反射率,并着重分析了不同健康状态稻穗的原始光谱、对数光谱、一阶和二阶微分光谱特征。利用主成分分析方法获取了前述多种变换光谱的主分量,并以其为输入向量,利用学习矢量量化神经网络对多种健康状态稻穗进行分类。结果显示:原始光谱、对数光谱、一阶和二阶微分光谱的总体分类精度分别为75.3%,74.7%,91.6%和100%,Kappa系数分别为0.689,0.682,0.895和1.000。研究表明,运用高光谱遥感技术对稻穗健康状态进行识别是切实可行的。

The incidence of disease and insect stresses in rice panicles at the late growth stage had a negative impact on normal growth of panicles.Discrimination of health conditions in stressed rice panicles from healthy ones was the basis of plant protection measurements and yield assessment.Five kinds of health condition in rice panicles were classified in the study：no infection,light and moderate infection caused by rice glume blight disease,serious infection caused by rice false smut disease,and white headed panicle caused by either the stem borers Chilo suppressalis or rice panicle blast.The wavelength range of hyperspectral reflectance of rice panicles was measured from 350 to 2 500 nm with a portable spectroradiometer in laboratory conditions.The spectral response characteristics of rice panicles were investigated,and principal component analysis（PCA） was performed to obtain the principal components（PCs） derived from different transforming spectra,namely raw（R）,inverse logarithmic（Ln 1/R）,first,and second derivative reflectance（FDR and SDR）.PCs was input into the learning vector quantization（LVQ） neural network classifier as the input vectors to classify healthy,light,moderate,and serious infection levels and white headed panicles.Classification accuracy was evaluated using overall accuracy and Kappa coefficient.The overall accuracies of LVQ with PCA derived from R,Ln 1/R,FDR and SDR for the validation dataset were 75.3%,74.7%,91.6% and 100% respectively,and the corresponding Kappa coefficients were 0.689,0.682,0.895 and 1.000.The results demonstrated that it is feasible to discriminate different health conditions of rice panicles under laboratory conditions using hyperspectral remote sensing.