Next Article in Journal
Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method
Previous Article in Journal
Spectrum- and RGB-D-Based Image Fusion for the Prediction of Nitrogen Accumulation in Wheat
Open AccessArticle

Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes

1
Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97331, USA
2
Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97333, USA
3
Dorena Genetic Resource Center, USDA Forest Service, Cottage Grove, OR 97424, USA
4
School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA
5
Department of Environmental Studies, Prescott College, Prescott, AZ 86301, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(24), 4041; https://doi.org/10.3390/rs12244041
Received: 22 October 2020 / Revised: 26 November 2020 / Accepted: 6 December 2020 / Published: 10 December 2020
Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (κ = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert’s assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a ‘growth vigor’ grouping used to identify the degree of impact of the disease was possible with 79.7% (κ = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a ‘new search algorithm’ that combines both the p-value of a 2-sample t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras. View Full-Text
Keywords: hyperspectral imaging; classification; disease detection; feature importance; family differences; phenotyping; Pinus strobiformis; Cronartium ribicola hyperspectral imaging; classification; disease detection; feature importance; family differences; phenotyping; Pinus strobiformis; Cronartium ribicola
Show Figures

Graphical abstract

MDPI and ACS Style

Haagsma, M.; Page, G.F.M.; Johnson, J.S.; Still, C.; Waring, K.M.; Sniezko, R.A.; Selker, J.S. Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes. Remote Sens. 2020, 12, 4041. https://doi.org/10.3390/rs12244041

AMA Style

Haagsma M, Page GFM, Johnson JS, Still C, Waring KM, Sniezko RA, Selker JS. Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes. Remote Sensing. 2020; 12(24):4041. https://doi.org/10.3390/rs12244041

Chicago/Turabian Style

Haagsma, Marja; Page, Gerald F.M.; Johnson, Jeremy S.; Still, Christopher; Waring, Kristen M.; Sniezko, Richard A.; Selker, John S. 2020. "Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes" Remote Sens. 12, no. 24: 4041. https://doi.org/10.3390/rs12244041

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop