Machine Learning-Based Hyperspectral and RGB Discrimination of Three Polyphagous Fungi Species Grown on Culture Media

Abstract

In this study, three fungi species (Botrytis cinerea, Rhizoctonia solani, Sclerotinia sclerotiorum) were discriminated using hyperspectral and red-green-blue (RGB) data and machine learning methods. The fungi were incubated at 25 °C for 10 days on potato dextrose agar in Petri dishes. The Hyperspectral data were acquired using an ASD spectroradiometer, which measures reflectance with 3 and 10 nm bandwidths over the range 350–1000 nm and the range 1000–2500 nm, respectively. The RGB images were collected using a digital Canon 450D camera equipped with the DIGIC 3 processor. The research showed the possibility of distinguishing the analysed fungi species based on hyperspectral curves and RGB images and assessing this differentiation using machine learning statistical methods (extreme boosting machine with bootstrap simulation). The best results in analysed fungi discrimination based on hyperspectral data were achieved using the Principal Component Analysis method, in which the average values of recognition and accuracy for all three species were 0.96 and 0.93, respectively. The wavelengths of the shortwave infrared (SWIR) wavelength region appeared to be the most effective in distinguishing B. cinerea-R. solani and B. cinerea-S. sclerotiorum, while of the visible range (VIS) of electromagnetic spectrum in discrimination of R. solani-S. sclerotiorum. The hyperspectral reflectance data were strongly correlated with the intensity of the pixels in the visible range (R2 = 0.894–0.984). The RGB images proved to be successfully used primarily for the identification of R. solani (recognition = 0.90, accuracy = 0.79) and S. sclerotiorum (recognition = 0.84, accuracy = 0.76). The greatest differences in the intensity of the pixels between B. cinerea and R. solani as well as R. solani and S. sclerotiorum occurred in the blue band and in distinguishing B. cinerea and S. sclerotiorum in the red band.