Olive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual “on year” Yield (AY) is production (kg tree⁻¹) in “on years”, and this research attempts to relate it with geometrical parameters of the tree canopy. Regression equation to forecast AY based on manual canopy volume was determined based on data acquired from different orchard categories and cultivars during different harvesting seasons in southern Spain. Orthoimages were acquired with unmanned aerial systems (UAS) imagery calculating individual crown for relating to canopy volume and AY. Yield levels did not vary between orchard categories; however, it did between irrigated orchards (7000–17,000 kg ha⁻¹) and rainfed ones (4000–7000 kg ha⁻¹). After that, manual canopy volume was related with the individual crown area of trees that were calculated by orthoimages acquired with UAS imagery. Finally, AY was forecasted using both manual canopy volume and individual tree crown area as main factors for olive productivity. AY forecast only by using individual crown area made it possible to get a simple and cheap forecast tool for a wide range of olive orchards. Finally, the acquired information was introduced in a thematic map describing spatial AY variability obtained from orthoimage analysis that may be a powerful tool for farmers, insurance systems, market forecasts or to detect agronomical problems.

Olive fruit production and oil quality distribution with respect to canopy distribution are important criteria for selection and improvement of mechanical harvesting methods. Tests were performed in a high-density olive orchard (Olea europea L., cv. Arbequina) in southern Spain. Fruit distribution, fruit properties and oil parameters were measured by taken separate samples for each canopy location and tree. Results showed a high percentage of fruits and oil located in the middle-outer and upper canopy, representing more than 60% of total production. The position of these fruits along with their higher weight per fruit, maturity index and polyphenol content make them the target for all mechanical harvesting systems. The fruits from the lower canopy represented close to 30% of fruit and oil production, however, the mechanical harvesting of these fruits is inefficient for mechanical harvesting systems. Whether these fruits cannot be properly harvested, enhance tree training to raise their position is recommended. Fruits located inside the canopy are not a target location for mechanical harvesting systems as they were a small percentage of the total fruit (<10%). Significant differences were found for polyphenol content with respect to canopy height, although this was not the case with acidity. In addition, the ripening index did not influence polyphenol content and acidity values within the canopy. Fruit production, properties and oil quality varied depending on fruit canopy position. Thus harvesting systems may be targeted at maximize harvesting efficiency including an adequate tree training system adapted to the harvesting system.