Early detection of black Sigatoka in banana leaves using hyperspectral images
| Autor: | Juan M. Cevallos-Cevallos, María Gabriela Maridueña-Zavala, Daniel Ochoa Donoso, José Luis Vicente Villardón, Jorge Ugarte Fajardo, Ronald Criollo Bonilla, Oswaldo Bayona Andrade |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
0301 basic medicine Application Article Black sigatoka Biplot hyperspectral imaging HS biplot Early detection Plant Science Biology 010603 evolutionary biology 01 natural sciences 03 medical and health sciences For the Special Issue: Advances in Plant Phenomics: From Data and Algorithms to Biological Insights lcsh:Botany Pseudocercospora fijiensis Application Articles lcsh:QH301-705.5 Ecology Evolution Behavior and Systematics Visual tool plant disease Invited Special Article External validation Hyperspectral imaging black Sigatoka penalized logistic regression (PLS–PLR) Plant disease lcsh:QK1-989 Horticulture 030104 developmental biology banana lcsh:Biology (General) |
| Zdroj: | Applications in Plant Sciences, Vol 8, Iss 8, Pp n/a-n/a (2020) Applications in Plant Sciences |
| ISSN: | 2168-0450 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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