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.

Global Navigation Satellite Systems (GNSS) allow the determination of the 3D position of a point on the Earth’s surface by measuring the distance from the receiver antenna to the orbital position of at least four satellites. Selecting and buying a GNSS receiver, depending on farm needs, is the first step for implementing precision agriculture. The aim of this work is to compare the positioning accuracy of four GNSS receivers, different for technical features and working modes: L1/L2 frequency survey-grade Real-Time Kinematic (RTK)-capable Stonex S7-G (S7); L1 frequency RTK-capable Stonex S5 (S5); L1 frequency Thales MobileMapper Pro (TMMP); low-cost L1 frequency Quanum GPS Logger V2 (QLV2). In order to evaluate the positioning accuracy of these receivers, i.e., the distance of the determined points from a reference trajectory, different tests, distinguished by the use or not of Real-Time Kinematic (RTK) differential correction data and/or an external antenna, were carried out. The results show that all satellite receivers tested carried out with the external antenna had an improvement in positioning accuracy. The Thales MobileMapper Pro satellite receiver showed the worst positioning accuracy. The low-cost Quanum GPS Logger V2 receiver surprisingly showed an average positioning error of only 0.550 m. The positioning accuracy of the above-mentioned receiver was slightly worse than that obtained using Stonex S7-G without the external antenna and differential correction (maximum positioning error 0.749 m). However, this accuracy was even better than that recorded using Stonex S5 without differential correction, both with and without the external antenna (average positioning error of 0.962 m and 1.368 m).

Precision beekeeping or precision apiculture is an apiary management strategy based on the monitoring of individual bee colonies to minimize resource consumption and maximize the productivity of bees. Bees play a fundamental role in ensuring pollination; they can also be considered as indicators of the state of pollution and are used as bio monitors. Beekeeping needs continuous monitoring of the animals and can benefit from advanced intelligent ambiance technologies. The aim of this study was the design of a precision apiculture system (PAS) platform for monitoring and controlling the following environmental parameters: wind, temperature, and relative humidity inside and outside the hive, in order to assess their influence on honey production. PAS is based on an Arduino board with an Atmel microcontroller, and the connection of a load cell for recording the weight of the hive, relative humidity and temperature sensor inside the hive, and relative humidity and temperature sensor outside the hive using an anemometer. PAS was installed in common hives and placed in an open field in a French honeysuckle plot; the system was developed to operate in continuous mode, monitoring the period of 24 April–1 June 2019. Temperature was constant in the monitored period, around 35 °C, inside the hive, proving that no criticalities occurred regarding swarming or absconding. In the period between 24 and 28 May, a lack of honey production was recorded, attributed to a lowering of the external temperature. PAS was useful to point out the eventual reduction in honey production due to wind; several peaks of windiness exceeding 5 m s⁻¹ were recorded, noting that honey production decreases with the peaks in wind. Therefore, the data recorded by PAS platform provided a valid decisional support to the operator. It can be implemented by inserting additional sensors for detecting other parameters, such as rain or sound.

Olive tree growing (Olea europaea L.) has considerably increased in the last decades, as has the consumption of extra virgin olive oil in the world. Precision agriculture is increasingly being applied in olive orchards as a new method to manage agronomic variability with the aim of providing individual plants with the right input amount, limiting waste or excess. The objective of this study was to develop a methodology on a GIS platform using GEOBIA algorithms in order to build prescription maps for variable rate (VRT) nitrogen fertilizers application in an olive orchard. The fertilization plan was determined for each tree by applying its own nitrogen balance, taking into account the variability of nitrogen in soil, leaf, production, and actual biometric and spectral conditions. Each olive tree was georeferenced using the S7-G Stonex instrument with real-time kinematic RTK positioning correction and the trunk cross section area (TCSA) was measured. Soil and leaves were sampled to study nutrient variability. Soil and plant samples were analyzed for all major physical and chemical properties. Spectral data were obtained using a multispectral camera (DJI multispectral) carried by an unmanned aerial vehicle (UAV) platform (DJI Phantom4). The biometric characteristics of the plants were extracted from the achieved normalized vegetation index (NDVI) map. The obtained prescription map can be used for variable rate fertilization with a tractor and fertilizer spreader connected via the ISOBUS system. Using the proposed methodology, the variable rate application of nitrogen fertilizer resulted in a 31% reduction in the amount to be applied in the olive orchard compared to the standard dose.