The digitization of viticulture in the last thirty years contributes to the rapid development of new technologies for monitoring production and the acceptance and adoption of new knowledge about the variability of vineyards and the possibility of using variability to increase the economic efficiency of production. The uniform vineyard management can be justified by the simplicity of carrying out all vineyard operations as long as the winegrower is satisfied with the final grape quality. Identifying different quality zones based on variations within the vineyard can allow winegrowers to obtain higher revenues from grape growing and wine production. By using unmanned aerial vehicles (UAV) equipped with multispectral cameras and analysing the collected data using vegetation indices, it is possible to make better decisions for differentiated vineyard management, maximizing the agronomic, oenological and economic impact of viticulture production. The main objective of this study was to determine the most predictive vegetation index for grape quality zoning, among three different vegetation indices (NDVI, NDRE, and OSAVI) at three different grapevine growth stages, which can be efficiently used in commercial vineyards for selective harvesting and the production of different wine types. In addition, an economic analysis of the costs and revenues of implementing grape quality zoning and selective harvesting in small vineyards (up to 1 ha) was performed, along with the calculations of the potential revenue increases after selective harvesting and production of different wine types from the same grapevine variety and vineyard. The first chapter, Introduction, focuses on the problem description and presents the main aspects of quality zonal management and the main hypotheses and objectives of the study. The hypotheses that were established and tested in this study are: (1) there is a difference in the relationship between vegetative indices (NDVI, NDRE, OSAVI) and vigour at three different grapevine growth stages (GS) for two grapevine varieties; (2) there is a relationship between different vigour zones and vegetative, yield, and grape quality components; (3) there is economic justification for using vegetative indices for vineyard quality zonal management and selective harvesting in vineyards up to 1 ha. The second chapter Overview of the Previous Research focuses on related previous research on vineyard variability, precision viticulture, remote sensing, vegetation indices, vineyard quality zonal management and relevant studies on the economic efficiency of precision viticulture. The main reason for studying vineyard variability is its influence on grape yield and quality. Variability can be caused by various factors (natural factors, human actions, factors beyond control, etc.), but regardless of its source, monitoring and managing of variability has the goal of helping winegrowers to achieve the greatest possible efficiency in viticulture production. In order to monitor the different parameters and the related changes in the vineyard throughout the growing season, many new tools and technologies are being developed and used in viticulture. There are two main groups of technologies that are being used: data-intensive technologies and automated technologies, which are differentiated according to the main outcomes that result from the use of each technology. Data-intensive technologies, such as vigour and yield monitoring and mapping, soil condition mapping, and environmental data collection can improve understanding of the factors causing variability, but also require additional skills to use the technologies effectively and it is much more difficult to estimate economic efficiency. Much research has been done on the capabilities of UAVs. UAVs have been used to assess grape yield and quality, water stress, photosynthetic activity, disease, pest and weed incidence, but also to detect missing vines, estimate vine height, estimate damage caused by flooding and drought, etc. In the last twenty years, much research has been done with vegetation indices (mainly NDVI), and these spectral vegetation measurements have been used to describe vine and grape characteristics such as vigour, yield, grape quality, health status, etc. Previous research has shown that vegetation indices can be good predictive tools for vigour-based quality management zones and for assessing vineyard variability. Some of the most commonly used vegetation indices in viticulture are NDVI, NDRE, and OSAVI. The relationship between vigour zones and yield and grape quality components has been confirmed in many previous studies, which showed that high vigour zones delineated mainly with NDVI had higher yield per vine, lower sugar concentration, and higher total titratable acidity than vines from low or medium vigour zones. Selective harvesting is defined as the split-picking of grapes at harvest according to different yield/quality criteria with the aim of producing different products to take advantage of the observed variability in vineyard performance. In the economic analysis, the investment in the new technology must be considered in relation to the expected benefits for the winegrowers. The potential profitability of precision farming can only be evaluated in comparison to management without precision farming. The economic aspect of selective harvesting mainly refers to the differentiation of grapes from different quality zones with different future wine prices. The third chapter Materials and Methods focuses on all the information on how the research was conducted. The research was conducted in 2019 and 2020 in the area of Zagreb County in four different locations and on four different vineyard areas of less than 1 ha (0.33 ha, 0.47 ha, 0.65 ha and 0.93 ha). Two grapevine varieties were included in the study - 'Kraljevina' (Vitis vinifera L.) in the Zelina winegrowing region and 'Pinot noir' (Vitis vinifera L.) in the Plešivica winegrowing region. Using a UAV DJI Inspire 1 Pro equipped with a multispectral camera (Micasense RedEdge.MXTM, Seattle, WA, USA) multispectral images were acquired each year at the experimental sites at three different grapevine GS: berries beginning to soften, Brix starts increasing (GS 34); berries with intermediate Brix values (GS 36); and berries harvest-ripe (GS 38). After each flight, images were processed using ATLAS MicaSense app (MicaSense, Seattle, DC, USA), Pix4D software package (Pix4D, Lausanne, Switzerland) and ArcGIS software package (Environmental Systems Research Institute (ESRI) (2012), ArcGIS Release 10.1, Redlands, CA, USA). Interpolated maps representing two vigour zones using NDVI, NDRE, or OSAVI were generated for each site and flight and used for subsequent statistical analyses. Manual sampling and measurements of yield and grape quality components were performed on pre-selected target vines. The number of target vines was determined according to the area of vineyard. Each target vine was assigned to a vigour zone (high or low) based on the two zones previously defined. In addition, for each map created, the percentage of vineyard area in low and high vigour zones was calculated to allow for economic efficiency calculations for grape quality zoning and selective harvesting based on the vegetation indices. The collected grape quality components were subjected to a clustering procedure at each site and in each year, with the K-means clustering procedure set for two clusters. In this way, two different grape quality clusters (clusters of better and inferior grape quality) were obtained and used for comparison with the target vine vigour zone structures (categorical variable) based on NDVI, NDRE, and OSAVI vigour maps at three different grapevine GSs. The target vines belonging to the low vigour zone (green in vigour maps) were characterised with better grape quality components, while the target vines belonging to the high vigour zone (red in vigour maps) were characterised with inferior grape quality components. The vegetative index whose classification structure of target vines vigour (categorical variable) most closely matched the cluster structure of determined grape quality was considered the most predictive. Using the survey method and secondary desk research, data were collected for the analysis of the economic efficiency of investment in quality zonal management and selective harvesting. Financial analyses of the investment for two scenarios: the winegrower performing quality zonal management himself and the winegrower as a user of a commercial service for quality zonal management. In the chapters Results and Discussion, all the research results are presented and discussed: interpolated maps for each site, each vegetation index, each GS and both years; results of the clustering procedure and of the overlap between two classification structures (vigour and grape quality) for the most predictive vegetation index, together with the descriptive statistical analysis and the results of testing the statistical significance of the differences between the two vigour zones based on the most predictive vegetation index. Finally, the results of the economic efficiency and financial analysis are presented. All costs (fixed and variable) and potential revenues from grape and wine production after selective harvesting were calculated. Investment analysis were prepared for all sites and two possible scenarios, profit or loss statements and relevant financial criteria for investment analysis were calculated (NPV, IRR, payback period, discounted payback period, mIRR). A two-year study on the possibility of using three different vegetation indices- NDVI, NDRE and OSAVI- for grape quality zoning has shown that they are effective tools for assessing vigour and variability in small vineyards in Zagreb County and that they can successfully describe yield and grape quality components and link them to the results of spectral measurements. However, there are differences in the effectiveness of estimating the relationship between vigour, yield, and grape quality components depending on the vegetation index used and the grapevine’s GS. The NDVI as the most commonly used vegetation index in previous studies, in this study also proved to be the most predictive for delineating vigour zones and grape quality zoning of vineyards. NDVI was the most predictive vegetation index four times (out of seven) and at all sites. In addition, the percentage of overlap of classification quality structures was 81%-93%, making it the most predictive vegetation index for quality zoning. Further research is needed to determine the ideal time period for UAV data acquisition depending on climatic conditions during the growing season. Although NDRE has been used very modestly in previous studies for delineating vigour zones and grape quality zoning of vineyards, it proved to be the most predictive vegetation index three times (out of seven) at three sites. At these sites, NDRE successfully delineated grape quality zones at later grapevine GSs, and the zones were statistically significant. It can be said that the use of NDRE for grape quality zoning of vineyards is sufficiently predictive for purposes such as selective harvesting in the growing season in which the data are collected. OSAVI, the least used vegetation index in previous studies, was also found to be the least predictive in this study for delineating vigour zones and grape quality zoning of vineyards. OSAVI was not the most predictive at any grapevine GS or any study site. These results are consistent with previous studies in which OSAVI was used primarily to estimate the nitrogen content in grapevines for targeted nitrogen fertiliser applications. The potential economic efficiency of grape quality zoning and selective harvesting was evident in this study. The potential sales revenue from the production of “super-premium” wines after selective harvest was 14% to 45% higher than the total revenue that could be generated from the production of “quality” wines at all sites. Considering that all the wineries are small, family-owned boutique wineries and the selling prices of their wines were above average, the important finding is that they can use high- and low-quality zones to produce two types of “superpremium” wines. In terms of investment, both grape quality zoning scenarios (A- the winegrower performs grape quality zoning himself, B- the winegrower uses a commercial service for grape quality zoning) resulted in potentially higher profits and the financial criteria suggested that this would be an investment that can bring higher profits in a very short payback period, despite the higher costs required to implement grape quality zoning and selective harvesting. Using a commercial service provider for grape quality zoning resulted in even higher potential profits and lower risks for the winegrower. It can be concluded that vegetation indices NDVI and NDRE are effective tools for vineyard quality zonal management and can enhance the economic efficiency of viticulture through selective harvesting and the production of two types of “super-premium” wines from small vineyard areas up to 1 ha. Further research could be considered for the use of OSAVI on images where row segmentation has already been done and where grapevine vegetation and soil data are already separated. In addition, the ideal UAV data acquisition period for grape quality zoning still needs to be studied.