3. Future agronomic techniques to optimize olive cultivation

The characterisation of geometric (e.g., height, projected and transversal areas, volume) and structural (e.g., vigour, vegetative growth habits) canopy parameters of individual olive trees in an orchard is highly valuable for many agronomic decision-making processes. For example, such data support the design and supervision of digitised operations such as pruning or fungicide application; facilitate the study of tree health in response to threats such as Xylella fastidiosa or Verticillium dahliae; and enable the monitoring and phenotyping of varietal performance in field breeding programmes. The latter is particularly focused on the development of new olive cultivars with targeted traits, such as resistance or tolerance to biotic and abiotic stresses, improved suitability for mechanisation (e.g., pruning, harvesting), and adaptability to high- and super-high-density orchards.

However, the measurement of early olive traits for phenotyping presents major challenges, mainly due to the complexity of field sampling and the logistical demands of recording high tree variability across large plots. The advancement of olive breeding programmes has increased the need for more efficient (rapid, low-cost and accurate) tools to alleviate the labour-intensive manual mapping of irregular olive canopies, a crucial step in identifying desirable genotypes as early as possible. This, in turn, contributes to reducing the high costs associated with maintaining large numbers of trees throughout the lengthy evaluation periods required by breeding programmes.

To address these challenges, remotely sensed data collected by drones or unmanned aerial vehicles (UAVs) have become one of the most widely used non-destructive technologies. UAVs offer the advantage of operating on demand at critical growth stages, flying at low altitudes with high image overlap, thus generating imagery and 3D models with the spatial and temporal resolutions required by breeding programmes. The objective is to rapidly phenotype large numbers of plots, providing georeferenced information in map and/or table format on the same day the UAV flights take place.

Recent technological advances have greatly improved UAV performance, particularly in flight autonomy (e.g., optimised batteries) and payload capacity (e.g., miniaturised sensors). For example, a low-cost UAV equipped with a lightweight RGB sensor (0.4 kg) can cover approximately 80 ha in just 15 minutes when flying at 30 metres altitude under light wind conditions.
Once UAV imagery is acquired, photogrammetric processing generates 3D point clouds (e.g., an average density of 7,000 points per m²), providing detailed height (Z-value) information for every coordinate (X,Y), enabling the estimation of geometric traits. The vast amount of crop data integrated into UAV-based 3D point clouds requires the development and application of automatic and robust analytical methods. This approach allows for the quantification of canopy height (Figure 1), width and volume even in very young olive trees.

This high-throughput phenotyping system not only enables the automatic detection of plant architectural traits and phenotypic differences among olive varieties, but can also be applied to nursery management (using very low-altitude flights, e.g., 10 m), studying the relationship between yield and crown volume, ranking varieties based on different geometric properties, or monitoring flowering calendars and dynamics.

In summary, the described protocol addresses the traditional bottleneck of plant phenotyping by facilitating the early elimination of undesirable genotypes, thereby reducing the duration and costs of the selection process. Notably, around 90% of the scientific literature on UAV-based phenotyping has been published in the last five years, confirming that UAV technology is becoming a key tool for enhancing the efficiency and performance of breeding programmes.
 

Figure 1. Example of the average canopy width of the ‘Picual’ olive cultivar at different heights, using a series of rootstocks to evaluate their dwarfing effect on the vigour transmitted to ‘Picual’ as a scion. The objective is to expand the limited range of cultivars suitable for high-density planting systems. Black lines represent the standard deviation of canopy width measurements. Source: Torres-Sánchez et al. (2022). Precision Agriculture, DOI 10.1007/s11119-021-09832-9.