Premise: Black Sigatoka is one of the most severe banana (Musa spp.) diseases worldwide, but no methods for the rapid early detection of this disease have been reported. This paper assesses the use of hyperspectral images for the development of a partial-least-squares penalized-logistic-regression (PLS-PLR) model and a hyperspectral biplot (HS biplot) as a visual tool for detecting the early stages of black Sigatoka disease.
Methods: Young (three-month-old) banana plants were inoculated with a conidia suspension of the black Sigatoka fungus (Pseudocercospora fijiensis). Selected infected and control plants were evaluated using a hyperspectral imaging system at wavelengths in the range of 386-1019 nm. PLS-PLR models were run on the hyperspectral data set. The prediction power was assessed using leave-one-out cross-validation as well as external validation.
Results: The PLS-PLR model was able to predict the presence of the disease with a 98% accuracy. The wavelengths with the highest contribution to the classification ranged from 577 to 651 nm and from 700 to 1019 nm.
Discussion: PLS-PLR and HS biplot effectively estimated the presence of black Sigatoka disease at the early stages and can be used to graphically represent the relationship between groups of leaves and both visible and near-infrared wavelengths.
Keywords: HS biplot; banana; black Sigatoka; hyperspectral imaging; penalized logistic regression (PLS–PLR); plant disease.
© 2020 Ugarte Fajardo et al. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America.