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Holm oak (Quercus ilex subsp. ballota (Desf.) Samp.) bark is a commonly used remedy to treat gastrointestinal disorders, throat and skin infections, hemorrhages, and dysentery. It has also been previously reported that its methanol extracts possess antibacterial activity, which can be related to the richness of Quercus spp. extracts in phenolic compounds, such as flavonoids and tannins. However, there is no information on the antifungal (including oomycete) properties of the bark from Q. ilex or its subspecies (ilex and ballota). In this work, we report the characterization of the aqueous ammonia extract of its bark by FTIR and GC-MS and the results of in vitro and ex situ inhibition tests against three phytopathogens. The main phytochemical components identified were inositols (19.5%), trans-squalene (13%), 4-butoxy-1-butanol (11.4%), gulopyranose (9.6%), lyxose (6.5%), 2,4-dimethyl-benzo[H]quinoline (5.1%), catechol (4.5%), and methoxyphenols (4.2%). The efficacy of the extract in controlling forest phytopathogens was tested in vitro against Fusarium circinatum (responsible for pitch canker of Pinus spp.), Cryphonectria parasitica (which causes chestnut blight), and Phytophthora cinnamomi (which causes ‘root and crown rot’ in a variety of hosts, including Castanea, conifers, Eucalyptus, Fagus, Juglans, Quercus, etc.), obtaining EC90 values of 322, 295, and 75 μg·mL−1, respectively, much lower than those attained for a commercial strobilurin fungicide (azoxystrobin). The extract was further tested ex situ against P. cinnamomi on artificially inoculated, excised stems of ‘Garnem’ almond rootstock, attaining complete protection at a dose of 782 μg·mL−1. The results suggest that holm oak bark extract may be a promising source of bioactive compounds against invasive forest pathogens, including the oomycete that is causing its decline, the so-called ‘seca’ in Spain.

Despite extensive research on the chemical composition of elderberries and their numerous uses in pharmaceutical, beverage, and food production, there is still a lack of knowledge about Sambucus nigra leaves and flowers’ antimicrobial activity against plant pathogens. In this study, the phytoconstituents of their aqueous ammonia extracts were first characterized by infrared spectroscopy and gas chromatography–mass spectrometry. The major phytocompounds identified in the flower extract were octyl 2-methylpropanoate; 3,5-dihydroxy-6-methyl-2,3-dihydropyran-4-one; propyl malonic acid; adenine; and 1-methyl-2-piperidinemethanol. Concerning the leaf extract, 1,6-anhydro-β-D-glucopyranose; oleic acid; 2,1,3-benzothiadiazole; 2,3-dihydro-benzofuran; and 4-((1E)-3-hydroxy-1-propenyl)-2-methoxyphenol and other phenol derivatives were the main constituents. The potential of the extracts to act as bioprotectants was then investigated against three almond tree pathogens: Diaporthe amygdali, Phytophthora megasperma, and Verticillium dahliae. In vitro tests showed higher activity of the flower extract, with EC90 values in the 241–984 μg·mL−1 range (depending on the pathogen) vs. 354–1322 μg·mL−1 for the leaf extract. In addition, the flower extract led to full protection against P. megasperma at a dose of 1875 μg·mL−1 in ex situ tests on artificially-infected excised almond stems. These inhibitory concentrations were lower than those of commercial fungicides. These findings suggest that S. nigra aerial organs may be susceptible to valorization as an alternative to synthetic fungicides for the protection of this important crop.

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Cork, an anatomic adaptation of the bark of Quercus suber L. through its suberization process, finds its main application in the production of bottle stoppers. Its processing results in a large waste stream of cork fragments, granulates, and dust, which may be susceptible to valorization. The work presented here explored the use of its extracts to inhibit the growth of phytopathogenic microorganisms associated with apple tree diseases. The in vitro antimicrobial activity of cork aqueous ammonia extract was assayed against four fungi, viz. Monilinia fructigena and M. laxa (brown rot), Neofussicoccum parvum (dieback), and Phytophthora cactorum (collar and root rot), and two bacteria, viz. Erwinia amylovora and Pseudomonas syringae pv. syringae, either alone or in combination with chitosan oligomers (COS). Effective concentration values of EC90 in the 675–3450 μg·mL−1 range, depending on the fungal pathogen, were obtained in growth inhibition tests, which were substantially improved for the conjugate complexes (340–801 μg·mL−1) as a result of strong synergism with COS. Similar enhanced behavior was also observed in antibacterial activity assays, with MIC values of 375 and 750 μg·mL−1 for the conjugate complexes against P. syringae pv. syringae and E. amylovora, respectively. This in vitro inhibitory activity was substantially higher than those exhibited by azoxystrobin and fosetyl-Al, which were tested for comparison purposes, and stood out among those reported for other natural compounds in the literature. The observed antimicrobial activity may be mainly attributed to the presence of glycerin and vanillic acid, identified by gas chromatography–mass spectroscopy. In the first step towards in-field application, the COS–Q. suber bark extract conjugate complex was further tested ex situ against P. cactorum on artificially inoculated excised stems of the ‘Garnem’ almond rootstock, achieving high protection at a dose of 3750 μg·mL−1. These results suggest that cork industrial leftovers may, thus, be a promising source of bioactive compounds for integrated pest management.

Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest’s flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.