Hyperspectral images were taken from March till October, 2018, of southwestern white pine (Pinus strobiformis), SWWP, seedlings of ten different seed-source families. Half of the seedlings were inoculated with white pine blister rust (Cronartium ribicola). Visual assessments of vigor coincided with hyperspectral data acquisition. The aim of the experiment was to use hyperspectral data to automaticaly and objectively identify infection and degree of infection in SWWP seedlings. Moreover, we developed and evaluated a feature importance algorithm to identify the most usefull hyperspectral features for classification tasks.
|Date made available||Oct 27 2020|
|Publisher||Oregon State University|