As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.

Introduction: The biology of the grapevine (Vitis vinifera L.) is clearly influenced by the climatic conditions of the growing environment, where temperature and light play a major role in modifying plant physiology. In the scenario of climatic changes, radiative excess, correlated to the increase in temperature, can concretely subject the photosynthetic apparatus to a condition of light saturation and cause a drastic reduction in photochemical efficiency, giving rise to chronic photoinhibition phenomena. Undoubtedly, the ripening behavior also undergo evident alterations; the problem of sugar ripening, which is often strongly accelerated, is induced not only by high temperatures but also by the excess concentration of carbon dioxide (CO₂), which results in a higher ripening. In addition, high berry temperatures favor a reduction in the concentration of organic acids. The reported trends indicate that the need for urgent action is closely linked to the future environmental impacts that these changes could have on the entire wine sector. In recent years, shade treatments have been applied to the vine canopy to overcome this issue.Methods: The objective of this study was to determine how artificial canopy shading affects the vines vegetative growth and the ripening processes of Vitis vinifera cv. Nero d’Avola during the 2019-2020 vegetative seasons. Three treatments were established: shading treatment with a green net (shade factor 27%), shading treatment with a white net (shade factor 32%), and untreated vines, thus naturally exposed to light radiation.Results and discussion: Artificial shading, applied at full fruit set, interfered with the microclimate of the vines, causing partial effects on the grape ripening processes. At harvest, significant differences were found between the treatments in terms of sugars, also shading treatments increased must acidity and decrease pH. Results obtained on vegetative parameters, suggest that the shading treatment delays leaf fall, with potentially positive effects on the starch accumulation on perennial reserve organs to be exploited at the following season’s sprouting. Shading significantly reduced berry size, with obvious consequences on bunch weight and yield per vine. In 2020, shaded plants showed a delay in all the phenological phases. The total anthocyanins content was not changed by the shading treatment. The results obtained confirm the importance of net coverage on the microclimate of the vines, vegetative-productive activity, and grapes quality. From this point of view, the net covering technique can be a tool for controlling grapes ripening dynamics in the context of climate change.

Climate change significantly affects viticulture, with noticeable impacts on yield and quality. The increase in average temperatures, often coupled with decreased precipitation, accelerates the phenological development of grapevines, leading to rapid sugar accumulation and concentration and decreased acidity. This study aimed to evaluate the impact of black shading nets with two levels (26% and 40%) on vine phenology, vegetative growth, yield, and grape ripening, as a potential strategy to mitigate the adverse effects of rising temperatures and reduced precipitation. The research was conducted in southwestern Sicily, in the Menfi (AG) area, using Grillo and Syrah grapevines. Black shading nets were applied during the pea-sized berry stage (BBCH 75). The results demonstrated that shading effectively delayed vine phenology and altered grape ripening, with significant reductions in sugar content (up to 10%) and increases in total acidity (up to 10%) at harvest compared to non-shaded vines. However, shading also reduced berry size, resulting in lower cluster weight and yield per plant (up to 15%). These findings highlight the potential of shading nets as a tool for adapting viticulture to climate change, while emphasizing the need to carefully assess their large-scale applicability, considering economic and operational factors.

Pruning wood mass is crucial for grapevine management, as it reflects the vine’s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially regarding the use of artificial backgrounds and lighting. This study assesses the use of image analysis for estimating wood weight, focusing on image acquisition conditions. This research aimed to (i) evaluate the necessity of artificial backgrounds and (ii) identify optimal daylight conditions for accurate image capture. Results demonstrated that estimation accuracy strongly depends on the sun’s position relative to the camera. The highest accuracy was achieved when the camera faced direct sunlight (morning on the northwest canopy side and afternoon on the southeast side), with R² values reaching 0.90 and 0.93, and RMSE as low as 44.24 g. Artificial backgrounds did not significantly enhance performance, suggesting that the method is applicable under field conditions. Leave-One-Group-Out Cross-Validation (LOGOCV) confirmed the model’s robustness when applied to Catarratto cv. (LOGOCV R² = 0.86 in NB and 0.84 in WB), though performance varied across other cultivars. These findings highlight the potential of automated image-based assessment for efficient vineyard management, using minimal effort adjustments to image collection that can be incorporated into low-cost setups for pruning wood weight estimation.