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Highlights • UAV mapping was economically beneficial when compared to traditional agriculture. • UAV missions can cover more surface than the covered by any on-ground platforms. • Increasing speeds for on-ground platforms would reduce the operating. • On-ground methods provide more detailed models than UAVs. Abstract Aerial and on-ground vegetation characterization technologies are continuously evolving. An experiment was conducted in a vineyard field in order to test the economic feasibility of applying fertilizers site specifically based on different mapping systems. The capacity of UAV missions and on-ground systems has been compared using depth cameras or LiDAR systems respectively in order to provide the necessary vineyard volume maps for specific applications like fertilization. Aerial imagery was obtained using a UAV equipped with a high-resolution RGB camera, and a digital surface model was reconstructed using photogrammetry procedures. On-ground crop reconstruction was performed using LiDAR-based measurements taken with an RTK-GNSS along the crop rows. Furthermore, a Kinect v2 sensor was also used as a low cost depth camera. All systems were tested in a commercial field, under sunlight conditions. Every technique provided a 3D dense point cloud from which volume was calculated. The results showed that volume values were always consistent and similar between the studied systems. The on-ground techniques provided the best details of the plants. However, the cost of acquisition was always higher than that of aerial imagery. Concerning the fertiliser application it should be noted that, the changes in shape and size of plants obtained within the vineyard indicate that continuous adjustment of the applied dose would be required to optimize the performed application. When using site-specific spraying based on the created maps, the dose was reduced by up to 80% of the total dosage used with a conventional application. A detailed analysis of savings indicates differences between the systems. The use of aerial imagery techniques resulted in positive net returns, whereas the on-ground technologies needed a faster time of acquisition in order of them to be profitable. Regarding efficacy, no significant differences between applications based on the constructed maps were found. This important reduction in fertilizer application could be followed by an equivalent reduction in plant protection products (e.g., fungicides). Thus, the use of some 3D characterization technologies has shown to be profitable at the current stage of development while also reducing the inputs and the environmental impact of agricultural tasks. [ABSTRACT FROM AUTHOR]

• Ground-based sensors aid Precision Viticulture to manage vineyards site-specifically. • A thorough review was conducted to present the latest ground-based sensor advances. • Non-optical and optical sensors are presented to characterize vine 2D and 3D traits. • Canopy characterization and vineyard yield forecasting are the most widely explored. Several variables, including a rising human population, varying weather patterns in the context of ongoing climate change, and the rapid worldwide spread of epidemics, all contribute to boosting agricultural demand. To assure food availability, quality, and safety while increasing yields and profitability, precision agriculture must progress swiftly. Precision viticulture aims to optimize vineyard management in this setting by reducing resource consumption and environmental impact while simultaneously enhancing the yield, product quality, and oenological potential of vineyards. This comprehensive review article offers an overview of the real-world and laboratory applications of optical and non-optical sensors in precision viticulture for 3D modelling. Hence, there is a pressing need to track the development of crops at a wide range of spatial and temporal scales, in a wide variety of environments, and for a wide range of objectives in a non-destructive manner. Due to the intrinsic spatial heterogeneity of vineyards, the adoption of precision viticulture necessitates crop monitoring using contactless and non-invasive sensors such as ultrasonic, LiDAR (Light Detection and Ranging), depth, or RGB cameras to prevent low accuracy and sparse sampling. This study aims to assist researchers in gaining a broad understanding of the sensing technologies for precision viticulture, the present problems, and the advancement of the state of the art. The study focuses on sensors used for Proximal Sensing to geometrically characterize vines using statically or dynamically ground-based measurements through a wide range of mobile sensing platforms. The employed sensors, data extraction, and analysis procedures are described. Moreover, the present and future potential of Proximal Sensing and Remote Sensing in vineyards is discussed. [ABSTRACT FROM AUTHOR]

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12-14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R² = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying. [ABSTRACT FROM AUTHOR]

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops. [ABSTRACT FROM AUTHOR]