Molecular changes associated with response to powdery mildew (PM) caused by Erysiphe necator have been largely explored in Vitis vinifera cultivars, but little is known on transcriptional and metabolic modifications following application of resistance elicitors against this disease. In this study, the whole transcriptome sequencing, and hormone and metabolite analyses were combined to dissect long-term defense mechanisms induced by molecular reprogramming events in PM-infected ‘Moscato’ and ‘Nebbiolo’ leaves treated with three resistance inducers: acibenzolar-S-methyl, potassium phosphonate, and laminarin. Although all compounds were effective in counteracting the disease, acibenzolar-S-methyl caused the most intense transcriptional modifications in both cultivars. These involved a strong down-regulation of photosynthesis and energy metabolism and changes in carbohydrate accumulation and partitioning that most likely shifted the plant growth-defense trade-off towards the establishment of disease resistance processes. It was also shown that genotype-associated metabolic signals significantly affected the cultivar defense machinery. Indeed, ‘Nebbiolo’ and ‘Moscato’ built up different defense strategies, often enhanced by the application of a specific elicitor, which resulted in either reinforcement of early defense mechanisms (e.g., epicuticular wax deposition and overexpression of pathogenesis-related genes in ‘Nebbiolo’), or accumulation of endogenous hormones and antimicrobial compounds (e.g., high content of abscisic acid, jasmonic acid, and viniferin in ‘Moscato’).

Drought stress is one of the major physiological stress factors that adversely affect agricultural production, altering critical features of plant growth and metabolism. Plants can be subjected simultaneously to abiotic and biotic stresses, such as drought and viral infections. Rewarding effects provided by viruses on the ability of host plants to endure abiotic stresses have been reported. Recently, begomoviruses causing the tomato yellow leaf curl disease in tomatoes were shown to increase heat and drought tolerance. However, biological bases underlying the induced drought tolerance need further elucidation, particularly in the case of tomato plants. In this work, tomato plants infected by the tomato yellow leaf curl Sardinia virus (TYLCSV) were subjected to severe drought stress, followed by recovery. Morphological traits, water potential, and hormone contents were measured in leaves together with molecular analysis of stress-responsive and hormone metabolism-related genes. Wilting symptoms appeared three days later in TYLCSV-infected plants compared to healthy controls and post-rehydration recovery was faster (2 vs. 4 days, respectively). Our study contributes new insights into the impact of viruses on the plant’s adaptability to environmental stresses. On a broader perspective, such information could have important practical implications for managing the effects of climate change on agroecosystems.

The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones. A dataset of 4112 images, each having 2Mpixel resolution per tree and covering RGB, Red Edge, and near-infrared frequencies, was obtained from 185 hazelnut trees located in two different orchards of the Piedmont region (northern Italy). To increase accuracy, and especially to reduce false negatives, the image of each tree was divided into nine quadrants. For each quadrant, nine different vegetation indices (VIs) were computed, and in parallel, each tree quadrant was tagged as “healthy/unhealthy” by visual inspection. Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. Out of the nine VIs considered, only five (GNDVI, GCI, NDREI, NRI, and GI) were good predictors, while NDVI SAVI, RECI, and TCARI were not. Using them, a model accuracy of about 65%, with 13% false negatives was reached in a way that was rather independent of the algorithms, demonstrating that some VIs allow inferring the physio-pathological condition of these trees. These achievements support the use of drone-captured images for performing a rapid, non-destructive physiological characterization of hazelnut trees. This approach offers a sustainable strategy for supporting farmers in their decision-making process during agricultural practices.