Effective Methods Based on Distinct Learning Principles for the Analysis of Hyperspectral Images to Detect Black Sigatoka Disease.

Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging.

Early detection of black Sigatoka in banana leaves using hyperspectral images.

Premise: Black Sigatoka is one of the most severe banana ( 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.