This page has only limited features, please log in for full access.
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.
Catello Pane; Gelsomina Manganiello; Nicola Nicastro; Teodoro Cardi; Francesco Carotenuto. Powdery Mildew Caused by Erysiphe cruciferarum on Wild Rocket (Diplotaxis tenuifolia): Hyperspectral Imaging and Machine Learning Modeling for Non-Destructive Disease Detection. Agriculture 2021, 11, 337 .
AMA StyleCatello Pane, Gelsomina Manganiello, Nicola Nicastro, Teodoro Cardi, Francesco Carotenuto. Powdery Mildew Caused by Erysiphe cruciferarum on Wild Rocket (Diplotaxis tenuifolia): Hyperspectral Imaging and Machine Learning Modeling for Non-Destructive Disease Detection. Agriculture. 2021; 11 (4):337.
Chicago/Turabian StyleCatello Pane; Gelsomina Manganiello; Nicola Nicastro; Teodoro Cardi; Francesco Carotenuto. 2021. "Powdery Mildew Caused by Erysiphe cruciferarum on Wild Rocket (Diplotaxis tenuifolia): Hyperspectral Imaging and Machine Learning Modeling for Non-Destructive Disease Detection." Agriculture 11, no. 4: 337.
Ornamental plant production constitutes an important sector of the horticultural industry worldwide and fungal infections, that dramatically affect the aesthetic quality of plants, can cause serious economic and crop losses. The need to reduce the use of pesticides for controlling fungal outbreaks requires the development of new sustainable strategies for pathogen control. In particular, early and accurate large-scale detection of occurring symptoms is critical to face the ambitious challenge of an effective, energy-saving, and precise disease management. Here, the new trends in digital-based detection and available tools to treat fungal infections are presented in comparison with conventional practices. Recent advances in molecular biology tools, spectroscopic and imaging technologies and fungal risk models based on microclimate trends are examined. The revised spectroscopic and imaging technologies were tested through a case study on rose plants showing important fungal diseases (i.e., spot spectroscopy, hyperspectral, multispectral, and thermal imaging, fluorescence sensors). The final aim was the examination of conventional practices and current e-tools to gain the early detection of plant diseases, the identification of timing and spacing for their proper management, reduction in crop losses through environmentally friendly and sustainable production systems. Moreover, future perspectives for enhancing the integration of all these approaches are discussed.
Silvia Traversari; Sonia Cacini; Angelica Galieni; Beatrice Nesi; Nicola Nicastro; Catello Pane. Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants. Sustainability 2021, 13, 3707 .
AMA StyleSilvia Traversari, Sonia Cacini, Angelica Galieni, Beatrice Nesi, Nicola Nicastro, Catello Pane. Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants. Sustainability. 2021; 13 (7):3707.
Chicago/Turabian StyleSilvia Traversari; Sonia Cacini; Angelica Galieni; Beatrice Nesi; Nicola Nicastro; Catello Pane. 2021. "Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants." Sustainability 13, no. 7: 3707.