The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

Purpose Yield mapping in viticulture is crucial for optimizing vineyard management. However, it remains constrained by limited yield monitoring sensors on grape harvesters and traditional remote sensing methods, which provide deterministic predictions without explicit uncertainty quantification. This study develops and evaluates a Bayesian hierarchical approach for vineyard yield mapping. Methods The model integrates multi-sensor remote sensing and mechanical harvester telemetry data to generate high-resolution, uncertainty-aware yield maps at the farm scale. Over two growing seasons (2022–2023) in a Sicilian vineyard. Normalized difference vegetation index data from unmanned aerial vehicles and Sentinel-2 satellites were combined with telemetry from global navigation satellite systems mounted on a mechanical grape harvester. Multiple model configurations were systematically evaluated using the brms package in R, and sensitivity analyses were performed. Results The best-performing model selected, based on six evaluation metrics (Bayesian R², RMSE, MAE, MAPE, and ELPD), achieved an R² of 0.83 and RMSE of 0.26 kg/vine on training data, with independent validation showing an R² of 0.78 and RMSE of 0.09. The model effectively captured spatial and temporal yield variations. Specifically, it identified a 15% yield decline in 2023 linked to drought and heat stress, while explicitly quantifying prediction uncertainty. Conclusion This hierarchical Bayesian approach significantly advances vineyard yield mapping, demonstrating robust generalization potential. Integrating additional data from multiple seasons, different vineyards, and grape cultivars could further enhance predictive accuracy, reduce uncertainty in prediction and improve scalability of the framework. Ultimately, this methodology offers valuable insights for precision viticulture, supporting targeted agronomic management and informed operational decisions.