Artificial intelligence and its deep learning techniques, due to their predictive potential, represent a strategic component of preventive innovation for the agricultural sector and its specialized activities such as viticulture, which is recognized for its high economic value to the agroindustry. The relevance of grape crops lies in their capacity to produce bioactive compounds, one of which is resveratrol, whose consumption could provide medical, pharmaceutical, and nutraceutical benefits in the prevention of cardiovascular diseases. However, resveratrol production is low because it is found in few red fruits, in low concentrations, and its preventive monitoring is based on destructive techniques that limit its availability, contribute to a supply lower than demand, and result in reduced intake. Therefore, and based on their predictive potential, the present study aims to analyze the performance of deep learning models to identify their performance in viticulture and the utilization of resveratrol following the PRISMA methodology. The review was based on studies published during the last 5 years in high-impact indexed journals, beginning with 82 proposals and concluding with 27. The literature reports the application of deep learning in crop yield prediction, grape cluster prediction, disease prediction, biochemical fruit analysis, and the mapping of physiological variables influencing the synthesis of the phenolic compounds; performance is high, showing superiority over traditional machine learning methods. Improved results were observed through hybrids such as CNN-LSTM, with CNN being the most widely applied. There is a large number of deep learning models in viticulture, but not in resveratrol research. Unaddressed predictive needs, representing the main research opportunities, include the multimodal approach and prediction focused on resveratrol.
Wineries log pH, temperature, and microbial counts sporadically yet need early alerts before malic acid plateaus. We train gradient boosted trees on five seasons of red-wine fermentations and use SHAP summaries to rank actionable predictors. The model flags likely stalls several days sooner than fixed acidity thresholds in retrospective cellar audits.
Regulators are tightening copper limits while growers fear yield loss from biological substitutes. We compile emission and soil burden inventories for six seasons of downy mildew programs, including fuel for sprayers and packaging of microbial products. Scenario analysis shows total metal load drops faster when biologicals pair with canopy management than when dose alone is reduced.
Micro-cracks in cork disks can fail after filling when line speeds increase. We instrumented compression stations with wideband acoustic sensors and labeled failures after six-month storage trials. Feature extraction from burst counts separates acceptable lots from prone batches earlier than visual grading at nominal throughput.
Shifting irrigation to off-peak hours can cut electricity bills but risks midday water stress on berries. We formulate a Pareto front between energy cost, predicted stem water potential, and labor windows, solved with epsilon-constraint search on historical weather ensembles. Recommended schedules reduce annual pumping charges without breaching quality thresholds used by export packers.
Highland communities ferment fruits in clay, gourd, and wooden vessels with poorly documented microbiota. We combine structured interviews with amplicon sequencing of starter residues and fresh musts. Vessel material explains a measurable share of fungal beta diversity, informing conservation of practices without prescribing uniform inoculation strategies.
Steam treatments must reach lethal temperatures inside stave gaps without damaging toast layers. We couple conduction through oak with transient condensation fluxes and validate profiles using embedded thermocouples on used barrels inoculated with Dekkera surrogates. Simulated schedules achieving six-log reduction shorten turnaround time relative to fixed-duration plant protocols.
Blended export lots can mask declared appellations when paperwork is incomplete. We measure hydrogen, carbon, and oxygen isotope ratios in ethanol and organic acids from reference vineyards and apply partial least squares discriminant analysis. Hold-out trials separate Baltic and Mediterranean training sets with fewer misclassifications than mineral-element panels alone.
Fungicide timing depends on spotting canopy hotspots before sporulation spreads between rows. We train a lightweight vision transformer on weekly multispectral mosaics from three commercial blocks and compare it with random forest on vegetation indices alone. The transformer raises recall for first-detection weeks while keeping false alerts below one block per hectare in cross-year validation.