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.

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.

Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection.

Baby leaf wild rocket cropping systems feeding the high convenience salad chain are prone to a set of disease agents that require management measures compatible with the sustainability-own features of the ready-to-eat food segment. In this light, bio-based disease resistance inducers able to elicit the plant’s defense mechanism(s) against a wide-spectrum of pathogens are proposed as safe and effective remedies as alternatives to synthetic fungicides, to be, however, implemented under practical field applications. Hyperspectral-based proximal sensing was applied here to detect plant reflectance response to treatment of wild rocket beds with Trichoderma atroviride strain TA35, laminarin-based Vacciplant^®, and Saccharomyces cerevisiae strain LAS117 cell wall extract-based Romeo^®, compared to a local standard approach including synthetic fungicides (i.e., cyprodinil, fludioxonil, mandipropamid, and metalaxyl-m) and a not-treated control. Variability of the spectral information acquired in VIS–NIR–SWIR regions per treatment was explained by three principal components associated with foliar absorption of water, structural characteristics of the vegetation, and the ecophysiological plant status. Therefore, the following model-based statistical approach returned the interpretation of the inducers’ performances at field scale consistent with their putative biological effects. The study stated that compost and laminarin-based treatments were the highest crop impacting ones, resulting in enhanced water intake and in stress-related pigment adjustment, respectively. Whereas plants under the conventional chemical management proved to be in better vigor and health status than the untreated control.