水稻冠层叶瘟病与缺氮的高光谱识别  

Hyperspectral Identification of Rice Canopy Leaf Blast and Nitrogen Deficiency

作　　者：袁建清 仇逊超 高睿 苏中滨[2] YUAN Jianqing;QIU Xunchao;GAO Rui;SU Zhongbin(Department of Computer Science,Harbin Finance University,Harbin 150030,China;College of Electrical and Information,Northeast Agricultural University,Harbin 150030,China)

机构地区：[1]哈尔滨金融学院计算机系,黑龙江哈尔滨150030 [2]东北农业大学电气与信息学院,黑龙江哈尔滨150030

出　　处：《云南农业大学学报(自然科学版)》2025年第1期167-175,共9页Journal of Yunnan Agricultural University:Natural Science

基　　金：科技创新2030-“新一代人工智能”重大项目(2021ZD0110904);黑龙江省省属本科高校基本科研业务费项目(2023-KYYWF-007,2020-KYYWF-E009)。

摘　　要：【目的】利用水稻大田近地冠层高光谱数据,实现在冠层尺度上对叶瘟病与缺氮水稻的早期识别。【方法】基于氮胁迫梯度试验和大田自然发病,采用室外高光谱成像系统采集缺氮水稻和自然发病水稻的大田近地冠层图像,提取并分析中度感病、轻度感病、缺氮和健康水稻冠层的反射率光谱特征。针对预处理后的高光谱数据,采用主成分分析(principal component analysis,PCA)、植被指数(vegetation index,VI)和竞争性自适应重加权法(competitive adaptive reweighted sampling,CARS)3种方法提取特征变量,并结合线性判别分析(linear discriminant analysis,LDA)与支持向量机(support vector machine,SVM)分类算法,构建水稻冠层叶瘟病与缺氮的早期识别模型。【结果】单独使用PCA降维方法提取特征构建的模型无显著的分类效果;基于VI的模型相比全谱模型在区分效果上有所提升,但提升幅度不大;采用CARS提取特征波长所构建的模型显示出最佳的区分效果。进一步对提取的CARS特征进行PCA降维后,获得5个主成分特征用于建模,其SVM和LDA模型的总体分类精度分别为95.51%和96.15%,Kappa系数分别为0.91和0.92,表现出较高的分类一致性。【结论】本研究通过选用较少的特征变量,成功实现了水稻冠层叶瘟病与缺氮的有效识别,为水稻病害与养分胁迫的遥感监测提供了新的思路和理论依据。研究成果可为大规模水稻病虫害监测与精准施药提供支持,在精准农业领域具有重要的应用价值。[Purpose]To achieve early detection of rice leaf blast and nitrogen deficiency at the canopy scale using hyperspectral data from rice field plots.[Methods]Based on nitrogen stress gradient experiments and natural field infection,hyperspectral images of nitrogen-deficient and naturally infected rice canopies were captured using an outdoor hyperspectral imaging system.Spectral reflectance features of moderate and mild infection,nitrogen-deficient,and healthy rice canopies were extracted and analyzed.For the preprocessed hyperspectral data,three feature extraction methods were applied,including principal component analysis(PCA),vegetation index(VI),and competitive adaptive reweighted sampling(CARS),and linear discriminant analysis(LDA)and support vector machine(SVM)classifiers were used to construct early detection models for rice leaf blast and nitrogen deficiency.[Results]The model constructed using PCA alone did not show significant classification performance.The models based on VI showed slight improvement over the full-spectrum models,but the enhancement was not substantial.The model constructed using features selected by the CARS method demonstrated the best discriminatory ability.Further dimensionality reduction of the extracted CARS features by PCA,five principal components were got and then used to construct the SVM and LDA models,which had overall classification accuracies of 95.51%and 96.15%,with Kappa coefficients of 0.91 and 0.92,respectively,demonstrating high classification consistency.[Conclusion]This study successfully achieves effective identification of rice leaf blast and nitrogen deficiency at the canopy scale using a limited number of feature variables.The results provide new insights and theoretical foundations for remote sensing monitoring of rice diseases and nutrient stress.The findings can support large-scale rice pest and disease monitoring and precision spraying,offering significant applications in the field of precision agriculture.

关 键 词：高光谱成像 水稻叶瘟病 竞争性自适应重加权法(CARS) 支持向量机(SVM) 线性判别分析(LDA) 

分 类 号：S435.111.1[农业科学—农业昆虫与害虫防治]