With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a substantial impact on the final product. This study’s main goals were to gather canopy morphology data using a sophisticated 3D model and assess how well different morphology characteristics predicted yield results. An unmanned aerial vehicle (UAV) with an RGB camera was used in the vineyards of Topoľčianky, Slovakia, to obtain precise orthophotos of individual vine rows. Following the creation of an extensive three-dimensional (3D) model of the assigned region, a thorough examination was carried out to determine many canopy characteristics, including thickness, side section dimensions, volume, and surface area. According to the study, the best combination for predicting grape production was the side section and thickness. Using more than one morphological parameter is advised for a more precise yield estimate as opposed to depending on only one.

Using UAV images for semiautomatic detection of row-gaps in vineyards in Jelenec and Topoľčianky (Slovakia) The use of UAV (Unmanned Aerial Vehicles) in precision viticulture leads to a more flexible and efficient approach to vineyard management. Images from UAV help determine the condition of the vineyard. Identification of the missing roots of the vineyard in a row by semi-automatic image classification and its comparison with manual classification is a goal of the paper. This study presents a new methodology for the segmentation of vine and row gaps. RGB (Red-Green-Blue) images, multispectral images, Near-Infrared (NIR) images, and Normalized Differential Vegetation Index (NDVI) images were tested and compared with manual classification. The percentage of row gap and the accuracy of individual images were determined. Object-oriented classification of the vine and row gaps in the buffer zone of the vineyard is a core of our method. Using geostatistical methods, such as zonal and logistic regression statistics, the accuracy of individual data in buffer zones was evaluated. Areas of interest were parts of vineyards in Jelenec and Topoľčianky. Success of the method detectomg outage (compared to manual classification) was achieved by images in the RGB spectrum: 96.45% for the Jelenec vineyard and 82.61% for the Topoľčianky vineyard. By this method, we quickly determine row gaps/vine which can be used to optimize or reduce the application of fertilizers to be used only on the vine. The method can be also used by inspection authorities to reveal the actual condition of the vineyard.

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance ...