Powdery Mildew Caused by Erysiphe cruciferarum on Wild Rocket ( Diplotaxis tenuifolia ): Hyperspectral Imaging and Machine Learning Modeling for Non-Destructive Disease Detection

Catello Pane, Gelsomina Manganiello, Nicola Nicastro, Teodoro Cardi and Francesco Carotenuto
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Catello Pane: Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy
Gelsomina Manganiello: Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy
Nicola Nicastro: Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy
Teodoro Cardi: Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro di Ricerca Orticoltura e Florovivaismo, Via Cavalleggeri, 25, 84098 Pontecagnano Faiano, Italy
Francesco Carotenuto: Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse, Università degli Studi di Napoli Federico II, Monte Sant’Angelo, Via Cinthia, 21, 80126 Napoli, Italy

Agriculture, 2021, vol. 11, issue 4, 1-15

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.

Keywords: chlorophyll absorption; digital detection; first report; hyperspectral signature; Random Forest model; XGBoost model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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