The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of “linking genotype and phenotype,” considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders.

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At a time of increasing demand, the extremely high cost of manual labor required to harvest fruit in table olive groves is limiting the economic survival of the crop in many producing countries. New grove designs and management practices such as superhigh-density (SHD) groves now in use in oil olive production should be explored as an option to facilitate mechanical harvesting in table olives. The feasibility of two table olive cultivars, Manzanilla de Sevilla and Manzanilla Cacereña, to be harvested in a 5-year-old SHD grove (1975 trees/ha) was studied in 2012 when trees of both cultivars formed highly productive continuous hedgerows (≈10,000 and 18,000 kg·ha−1, respectively). The differences between manual and mechanical harvesting using a grape straddle harvester were evaluated taking into consideration harvesting time, efficiency in fruit removal, and fruit quality both before and after processing as Spanish-style green olives. The average harvest time per hectare with a grape straddle harvester was less than 1.7 hours compared with 576 person/hour or more when done manually. Fruit removal efficiency was high in both cases, 98% for mechanical treatment and 100% for hand treatment. Mechanically harvested fruits had a high proportion of bruising damage (greater than 90%) and the severity of the damage was greater in ‘Manzanilla de Sevilla’ than in ‘Manzanilla Cacereña’. After Spanish-style green processing, however, the proportion of bruised fruits was below 3% in each cultivar. The fruit size in both cultivars was suitable for table olive processing and only 7% and 4% of ‘Manzanilla de Sevilla’ and ‘Manzanilla Cacereña’ fruits, respectively, were diverted to oil extraction as a result of insufficient size. Small differences were found between processed ‘Manzanilla Cacereña’ fruits that were manually or mechanically harvested. In contrast, mechanically harvested ‘Manzanilla de Sevilla’ fruits showed a significantly higher proportion of cutting (18%), a type of damage that may take place during harvesting, and lower firmness and texture than those harvested manually.

The relationship between the length of the juvenile period and nine olive seedling parameters (plant height, diameter, number of nodes, lateral shoots, internode length, leaf length, width, area, and shape index) was explored in 287 plants belonging to four different progeny. The traits were measured at two timepoints: after the plants had completed a forced growth cycle in the greenhouse/shadehouse (15 months after sowing) and after one growing season in the field (27 months after sowing). Strong linear tendencies of most vigour traits (mainly plant height and diameter) with the time of first flowering were observed. Leaf traits measured after one year in the field were also related to the length of the juvenile period, but not the same traits that were measured the previous year. Strong positive correlations were observed between the parameters studied. All results are discussed in terms of selecting the best seedling traits to be used as pre-selection criteria for short juvenile period during early stages.

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New super-high-density (SHD) olive orchards designed for mechanical harvesting using over-the-row harvesters are becoming increasingly common around the world. Some studies regarding olive SHD harvesting have focused on the effective removal of the olive fruits; however, the energy applied to the canopy by the harvesting machine that can result in fruit damage, structural damage or extra stress on the trees has been little studied. Using conventional analyses, this study investigates the effects of different nominal speeds and beating frequencies on the removal efficiency and the potential for fruit damage, and it uses remote sensing to determine changes in the plant structures of two varieties of olive trees (‘Manzanilla Cacereña’ and ‘Manzanilla de Sevilla’) planted in SHD orchards harvested by an over-the-row harvester. ‘Manzanilla de Sevilla’ fruit was the least tolerant to damage, and for this variety, harvesting at the highest nominal speed led to the greatest percentage of fruits with cuts. Different vibration patterns were applied to the olive trees and were evaluated using triaxial accelerometers. The use of two light detection and ranging (LiDAR) sensing devices allowed us to evaluate structural changes in the studied olive trees. Before- and after-harvest measurements revealed significant differences in the LiDAR data analysis, particularly at the highest nominal speed. The results of this work show that the operating conditions of the harvester are key to minimising fruit damage and that a rapid estimate of the damage produced by an over-the-row harvester with contactless sensing could provide useful information for automatically adjusting the machine parameters in individual olive groves in the future.

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