Powdery mildew caused by erysiphe cruciferarum onwild rocket (Diplotaxis tenuifolia): Hyperspectral imaging and machine learning modeling for non-destructive disease detection

Abstract

Wild rocket is a widely cultivated salad crop. Typical signs and symptoms of powdery mildew were observed on leaves of Diplotaxis tenuifolia, likely favored by climatic conditions occurring in a greenhouse. Based on morphological features and molecular analysis, the disease agent was identified as the fungal pathogen Erysiphe cruciferarum. To the best of our knowledge, this is the first report of E. cruciferarum on D. tenuifolia. Moreover, the present study provides a non-destructive high performing digital approach to efficiently detect the disease. Hyperspectral image analysis allowed to characterize the spectral response of wild rocket affected by powdery mildew and the adopted machine-learning approach (a trained Random Forest model with the four most contributory wavelengths falling in the range 403-446 nm) proved to be able to accurately discriminate between healthy and diseased wild rocket leaves. Shifts in the irradiance absorption by chlorophyll a of diseased leaves in the spectrum blue range seems to be at the base of the hyperspectral imaging detection of wild rocket powdery mildew.