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.

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COVID-19 pandemic poses a threat to global health highlighting the importance of prevention and measures of social distancing. In agriculture, cultivation operations carried out in open field by farm workers represent a serious danger in this sense. Social distancing of the workers during the labor day is not always easy to be maintained, especially for the very close rows among the plants. In 2020, the researchers of the Mechanics Section of the Department of Agricultural, Food and Forest Sciences in collaboration with the Department of Engineering of the University of Palermo, presented a project entitled "Design of a real time Monitoring and control system for AGrIcultural workers to limit the SARS-Cov-2 virus" (acronym MAGIC) to the Italian Ministry of University and Research. The aim of the project was the design of a real time monitoring and control system for workers in agriculture in order to monitor, record and control any violations of the distance among the workers. The system is based on the use of a small device equipped with a uniquely stored serial number that securely emits an alarm signal to both the worker and the manager when the distance between two employees is below the permitted threshold. A central manager perform a historical "track" that lists all the subjects with whom a person has come into close contact. The system was tested at a farm in Sicily during winter pruning in vineyard..

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