High-Resolution UAV Imagery for Field Olive (Olea europaea L.) Phenotyping

Remote sensing techniques based on images acquired from unmanned aerial vehicles (UAVs) could represent an effective tool to speed up the data acquisition process in phenotyping trials and, consequently, to reduce the time and cost of the field work. In this study, we assessed the ability of a UAV equipped with RGB-NIR cameras in highlighting differences in geometrical and spectral canopy characteristics between eight olive cultivars planted at different planting distances in a hedgerow olive orchard. The relationships between measured and estimated canopy height, projected canopy area and canopy volume were linear regardless of the different cultivars and planting distances (RMSE of 0.12 m, 0.44 m(2) and 0.68 m(3), respectively). A good relationship (R-2 = 0.95) was found between the pruning mass material weighted on the ground and its volume estimated by aerial images. NDVI measured in February 2019 was related to fruit yield per tree measured in November 2018, whereas no relationships were observed with the fruit yield measured in November 2019 due to abiotic and biotic stresses that occurred before harvest. These results confirm the reliability of UAV imagery and structure from motion techniques in estimating the olive geometrical canopy characteristics and suggest further potential applications of UAVs in early discrimination of yield efficiency between different cultivars and in estimating the pruning material volume.

Automated summary

Remote sensing techniques using images captured by UAVs are effective in highlighting differences in geometric and spectral characteristics of olive trees. The study shows that UAVs can linearly estimate canopy features and accurately estimate pruning material volume.