Nicola Furnitto

Vineyards are among the most globally significant cropping systems, and water stress is one of the major abiotic factors influencing both yield and grape quality. Adequate diagnosis of vine water status is, thus, imperative for efficient water management. In this context, thermal remote sensing enables monitoring large areas and the retrieval of indicators related to plant water stress and water use, providing useful information for decision support. This systematic review synthesizes the main applications of thermal remote sensing in viticulture for assessing vine water status and estimating Evapotranspiration (ET), using satellite and aerial platforms, while describing the most widely adopted workflows. These include (i) Surface Energy Balance model approaches for ET estimation, (ii) Crop Thermal Indexes for drought stress level assessment, and (iii) recent advances integrating Machine-Learning techniques. The literature highlights the strong potential of these approaches to identify different severities of water stress and to determine ET in large areas while also revealing recurring limitations associated with processing workflows, data calibration, as well as model transferability across sites and conditions. In addition, the need to identify thermal indicators that are closely linked to physiological variables is highlighted. Future research should focus on integrating these methods into operational Variable-Rate Irrigation strategies and Regulated Deficit Irrigation applications, with the aim of improving grape quality while safeguarding water resources.

The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGB_fresh) and dry above-ground biomass (AGB_dry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R² and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGB_fresh (R² = 0.89), with ARVI2 providing the lower RMSE (2047 g m⁻²). For AGB_dry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R² = 0.85 with RMSE = 1371 g m⁻². Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability.

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future.

Radiometric calibration of multispectral imagery plays a critical role in the determination of vegetation-related features. This radiometric calibration strongly depends on a proper sensor configuration when acquiring images, the shutter speed being a critical parameter. The objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3) on a homogeneous field of turfgrass (Cynodon dactylon L.—Poaceae) and on the vegetation index (VI) values calculated from them, under different solar radiation conditions. For this purpose, 10 shutter speed configurations were tested in field campaigns with variable solar radiation values. The main results demonstrated that the reflectance in the green spectral region was more sensitive to shutter speed than that of the red and NIR spectral regions, particularly under high solar radiation conditions. Moreover, VIs calculated using the green band were more sensitive to slow shutter speeds, thus presenting a higher probability of providing meaningless artifact values. In conclusion, this study provides shutter speed recommendations under different illumination conditions to optimize the reflectance and the VI sensitivity within the image, which can be applied as a simple method to optimize image acquisition from unmanned aerial vehicles under varying solar radiation conditions.

The adoption of agricultural robots is revolutionizing the agricultural sector, offering innovative solutions to optimize production and reduce environmental impact. This review examines the main functions and applications of agricultural robots, with a focus on the crops handled and the technologies employed. The study analyzes the current state of the art regarding the market trend of agricultural robots used in field and greenhouse operations. Several solutions are emerging, some already implemented and others still in the prototype or project stage. These solutions are beginning to spread, though they may still seem far from widespread field application, particularly given the peculiarities and heterogeneity of the global agricultural landscape. In the face of the many benefits associated with the use of agricultural robots, even today some technical bottlenecks and costs limit their widespread use by farmers. The review provides a fairly comprehensive and up-to-date overview of current trends in agricultural automation, suggesting new areas of research to improve the efficiency and adaptability of robotic systems to different types of crops and environments.

Precision agriculture has recently experienced significant advancements through the use of technologies such as unmanned aerial vehicles (UAVs) and satellite imagery, enabling more efficient and precise agricultural management. Yield estimation from these technologies is essential for optimizing resource allocation, improving harvest logistics, and supporting decision-making for sustainable vineyard management. This study aimed to evaluate grape cluster numbers estimated by using YOLOv7x in combination with images obtained by UAVs from a vineyard. Additionally, the capability of several vegetation indices calculated from Sentinel-2 and PlanetScope satellites to estimate grape clusters was evaluated. The results showed that the application of the YOLOv7x model to RGB images acquired from UAVs was able to accurately predict grape cluster numbers (R² value and RMSE value of 0.64 and 0.78 clusters vine⁻¹). On the contrary, vegetation indexes derived from Sentinel-2 and PlanetScope satellites were found not able to predict grape cluster numbers (R² lower than 0.23), probably due to the fact that these indexes are more related to vegetation vigor, which is not always related to yield parameters (e.g., cluster number). This study suggests that the combination of high-resolution UAV images, multispectral satellite images, and advanced detection models like YOLOv7x can significantly improve the accuracy of vineyard management, resulting in more efficient and sustainable agriculture.