13 th European Conference on Precision Agriculture, July 19 – July 22, 2021 The University of Public Service, Budapest, Hungary Paper submitted: 11 Dec 2020, Accepted: 13 April 2021 Page 1 VIs-image segmentation method for the estimation of agronomic traits in durum- and winter-wheat cultivars Stefano Marino*, Uzair Ahmad, Arturo Alvino Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, 86100 Campobasso, Italy *stefanomarino@unimol.it 13 th European Conference on Precision Agriculture July 19 – July 22, 2021 The University of Public Service Budapest, Hungary Abstract Vegetation indices (VIs) acquired by unmanned aerial vehicles (UAV) images are mainly associated with yield, yield components, and crop physiological responses. Unfortunately, simple strategies for grouping planting areas with homogeneous agronomic crop traits are still to be explored. The present study analyses the ability of cluster method applied to three VIs data (NDVI, SAVI and OSAVI) collected by high-resolution UAV at flowering stage to detect yield of seventeen cultivars (10 winter and 7 durum wheat varieties). Five VIs clusters were identified by Ward’s method, while the ground- truth data (150 samples) were analysed for cluster validation. Yield of the 1 st cluster was over 7 t ha −1 and the 5 th cluster showed 4 t ha −1 . As expected, yield differences were recorded between durum and winter wheat cultivars. Pooled data (winter + durum) showed a significant trend in the regression VIs-yield. The significance of this trend improved in the case of exclusion from pooled data of those cultivars showing peculiar spectro-radiometric response. Cluster identified by high-resolution UAV for determining vegetation indices can be a valid strategy although its effectiveness is closely linked to the cultivar component and, therefore, requires extensive verification, since the genetic component can affect the accuracy. Keywords Crop yield; Cluster analysis; Precision Agriculture; Vegetation indices; Wheat cultivars; Unmanned aerial vehicle