基于LMPSO-SVM的高光谱水稻稻瘟病害分级检测

Classification Detection of Hyperspectral Rice Blast Disease Based on LMPSO SVM

为减少水稻产量损失,迫切需要建立快速、准确的水稻叶瘟监测和鉴别方法。本文以东北水稻为研究对象,以小区试验为基础,使用高光谱图像仪获取受稻瘟病菌侵染后不同发病程度的水稻叶片高光谱图像并提取光谱数据。首先,通过SG平滑方法对光谱数据进行预处理,然后运用主成分分析(PCA)、Pearson相关系数分析法(PCCs)、PLS-VIP方法对光谱数据进行降维,并提出了一种基于Logistic混沌映射PSO寻优的SVM分级检测模型(LMPSO-SVM)。为了验证提出方法的有效性,以不同降维方法提取的特征变量为输入,分别建立基于人工神经网络(ANN)、支持向量机(SVM)和PSO-SVM的分级模型并进行对比分析。仿真结果表明,各模型对4级病害的识别效果最好,综合5种级别病害,SVM和ANN分级模型的预测准确率波动相对较大,对于病害预测效果不太理想;而在不同特征选择下建立的LMPSO-SVM分级模型对各级病害预测准确率均较高,准确率波动较小,其中基于PCA提取特征变量和全波段作为输入的模型平均准确率非常相近,分别为96.49%和96.12%,PCA提取的输入变量仅为5个,大大简化了模型复杂性,降低了训练难度和训练时间。综合分析,PCA-LMPSO-SVM模型的训练效果最好,可以认为是最佳病害分级模型,其5种级别病害准确率分别为94.29%、96.43%、93.44%、98.30%和100%。因此,本文提出的方法可进一步提高水稻稻瘟病分级检测精度和可靠性,结果可为确定稻瘟病发生情况提供一定的理论基础和技术支撑。

Rice blast is one of the three major rice diseases in the world,which poses a serious threat to food security of China.In order to reduce yield loss,it is urgent to establish a rapid and accurate method for monitoring and identifying rice leaf blast.Rice in northeast China is taken as the research object.Based on a plot experiment,hyperspectral images of rice leaves with different degrees of disease after infection by rice blast fungus were obtained through hyperspectral image analyzer,and spectral data was extracted.Firstly,the SG smoothing method was used to preprocess the spectral data,and then principal component analysis(PCA),Pearson correlation coefficient analysis(PCCs),and PLS VIP method were used to reduce the dimensionality of the spectral data.An SVM classification detection model based on Logistic chaotic mapping PSO optimization(LMPSO SVM)was proposed.To verify the effectiveness of the proposed method,classification models based on artificial neural network(ANN),support vector machine(SVM)and particle swarm optimization support vector machine(PSO SVM)were established by using feature variables extracted by different dimensionality reduction methods,and were compared and analyzed.The simulation results showed that each model had the best detection performance for level 4 samples.For these five levels of diseases,the prediction accuracy of SVM and ANN classification models fluctuated relatively large,and the effect of disease prediction was not ideal.The LMPSO SVM classification model established under different feature selection had high accuracy for disease prediction at all levels,and the accuracy fluctuated less.The average accuracy of the model based on PCA extraction of feature variables and the whole band as input was very similar,with 96.49%and 96.12%,respectively.However,the number of input variables extracted by PCA was only 5,which greatly simplified the model complexity,reduced the difficulty and time of training.Comprehensive analysis showed that the PCA LMPSO SVM model had the besttraining effect and could be considered as the best disease classification model.The accuracy rates for the five levels of diseases were 94.29%,96.43%,93.44%,98.30%and 100%,respectively.Therefore,the proposed method could further improve the accuracy and reliability of rice blast classification detection,and the results could provide a certain theoretical basis and technical support for the occurrence of rice blast diseases.