Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture

Abstract

New technologies for management, monitoring, and control of spatio-temporal crop variability in precision viticulture scenarios are numerous. Remote sensing relies on sensors able to provide useful data for the improvement of management efficiency and the optimization of inputs. unmanned aerial systems (UASs) are the newest and most versatile tools, characterized by high precision and accuracy, flexibility, and low operating costs. The work aims at providing a complete overview of the application of UASs in precision viticulture, focusing on the different application purposes, the applied equipment, the potential of technologies combined with UASs for identifying vineyards’ variability. The review discusses the potential of UASs in viticulture by distinguishing five areas of application: rows segmentation and crop features detection techniques; vineyard variability monitoring; estimation of row area and volume; disease detection; vigor and prescription maps creation. Technological innovation and low purchase costs make UASs the core tools for decision support in the customary use by winegrowers. The ability of the systems to respond to the current demands for the acquisition of digital technologies in agricultural fields makes UASs a candidate to play an increasingly important role in future scenarios of viticulture application.

QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

“…A high-density point cloud was generated, and from its interpolation, a DSM and then a DTM were computed. After the performance of the radiometric calibration, reflectance maps of each band are generated, and the NDVI [11] is computed using the reflectance from the NIR and red bands, as in Equation (2). A CSM was computed from the subtraction of the altitude values of the DTM to the DSM, as in Equation (3).…”

“…According to the definition adopted by the International Society of Precision Agriculture, Precision Agriculture (PA) can be defined as the "management strategy that gathers, processes and analyses temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production" [1]. Sassu et al [2] argue that PA is related with the use of technology to manage the spatial and temporal variability associated with agricultural production, so that it can improve the crop performance, bringing economic benefits and environmental quality with the correct use of pollutants. Regardless of the sources used to establish PA, they all include several innovative technologies, such as the use of data acquired by Unmanned Aerial Vehicles (UAVs), satellite imagery data, the use of Geographical Information Systems (GIS) technologies, nutrient management field mapping, Internet of Things (IoT) sensors, and data processing techniques such as Machine Learning and Deep Learning, among others, allowing experts to use specific tools to optimize agriculture production [2][3][4].…”

“…Sassu et al [2] argue that PA is related with the use of technology to manage the spatial and temporal variability associated with agricultural production, so that it can improve the crop performance, bringing economic benefits and environmental quality with the correct use of pollutants. Regardless of the sources used to establish PA, they all include several innovative technologies, such as the use of data acquired by Unmanned Aerial Vehicles (UAVs), satellite imagery data, the use of Geographical Information Systems (GIS) technologies, nutrient management field mapping, Internet of Things (IoT) sensors, and data processing techniques such as Machine Learning and Deep Learning, among others, allowing experts to use specific tools to optimize agriculture production [2][3][4]. Rmote sensing imagery, such as Sentinel-2 images or Landsat, combined with environmental data (climate, soil, elevation) have been used to forecast agriculture production [5,6].…”

Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery

“…Differences in their management, accuracy, economical cost, and other important factors have been extensively discussed, yielding various advantages and disadvantagesdirectly related to the targeted crop and conditions-of both methods. Although satellite image acquisition of large areas saves considerable time, it has a low and inadequate resolution for precision viticulture [23,24]. The effectiveness of Sentinel-2 imagery and high-resolution UAV aerial images was evaluated [25], concluding that the resolution of the satellite imagery was insufficient for their direct use for describing vineyard variability.…”

Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards

“…In case of remotely sensed imagery, the UAV (unmanned aerial vehicle) is being increasingly used for accurately monitoring of crops [ 9 , 10 , 11 ] due to its very high spatial and temporal resolution, low cost, easy access to difficult zones and absence of soil compaction. All these characteristics make UAV technology an adequate tool for mapping perennial crops such as almond [ 12 , 13 ], olive [ 14 , 15 ], or vineyards at the field scale for different proposes related with—e.g., 3D canopy characterization, water stress, or site-specific weed control [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. As happens with on-ground images, one of the most challenging objectives for UAV image analysis is to develop cost-effective, robust and straightforward repeatable procedures for vineyard yield estimation at field scale.…”