title: Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.
abstract: It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Qin
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Liu
authors.initials: L
authors.lastname: Ruan
authors.affiliation: Kaifeng Experimental Station of China Agricultural University, Kaifeng, Henan Province, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Zhanhong
authors.initials: Z
authors.lastname: Ma
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Pei
authors.initials: P
authors.lastname: Cheng
authors.affiliation: Department of Plant Pathology, China Agricultural University, Beijing, China.
authors.forename: Haiguang
authors.initials: H
authors.lastname: Wang
mesh_terms: D001487:Basidiomycota / Q000145:classification / Q000302:isolation & purification* / Q000472:pathogenicity; D002681:China; D016018:Least-Squares Analysis; D010209:Paper; D010935:Plant Diseases / Q000145:classification / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology; D058998:Remote Sensing Technology / Q000379:methods; D013053:Spectrophotometry; D060388:Support Vector Machine; D014908:Triticum / Q000382:microbiology*