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Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method
- Source :
- PLoS ONE, PLoS ONE, Vol 11, Iss 4, p e0154648 (2016)
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
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.
- Subjects :
- 0106 biological sciences
Leaves
Support Vector Machine
Light
lcsh:Medicine
Plant Science
01 natural sciences
Rust
Remote Sensing
Machine Learning
Partial least squares regression
Statistics
lcsh:Science
Triticum
Multidisciplinary
biology
Plant Anatomy
Physics
Electromagnetic Radiation
Plant Fungal Pathogens
food and beverages
Hyperspectral imaging
Agriculture
04 agricultural and veterinary sciences
Plants
Data Acquisition
Spectrophotometry
Wheat
Physical Sciences
Engineering and Technology
Research Article
Paper
Computer and Information Sciences
China
Mean squared error
Plant Pathogens
Crops
Wheat Stripe Rust
Cross-validation
Wheat leaf rust
Artificial Intelligence
Support Vector Machines
Grasses
Least-Squares Analysis
Plant Diseases
Basidiomycota
lcsh:R
Organisms
Biology and Life Sciences
Plant Pathology
biology.organism_classification
Plant Leaves
Support vector machine
Agronomy
Remote Sensing Technology
040103 agronomy & agriculture
Cognitive Science
0401 agriculture, forestry, and fisheries
lcsh:Q
Data pre-processing
Crop Science
Cereal Crops
Neuroscience
010606 plant biology & botany
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....5ce800a7ddad5f0a92801ca3b1cf45e1