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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.

The production of ‘Premium’ olive oil depends in large part on the quality of the fruit. Small producers see themselves confronted with vast investments and logistic snags when they intend to optimize the harvesting. Today, manual harvesting devices promise less damaged fruit when compared to the traditional methods with nets while the use of a cooling room on the farm is suggested as a solution when the harvesting needs to be stretched out over several days. The use of a manual inverted umbrella during the harvest, together with a storage of up to 14 days at 5 °C, was studied for three cultivars (‘Arbequina’, ‘Picual’, and ‘Verdial’). Ten parameters of the produced oil were examined in two consecutive years together with an extended sensory analysis in the first year. The results underline the importance of the used harvesting and conservation method on the quality of the extracted oil, although the effect size of each factor varied in time and according to the cultivar. The results indicate that small producers with financial and logistic restrictions can obtain a high-quality product following the actions shown in this work, being able to compete in terms of quality in the market, either by combining both methods or by choosing the one that guarantees the best results given the cultivar and the specific storage time they need to consider.

certain operations, particularly fruit harvesting. Nevertheless, traditional plantations are widespread globally and play an important social and environmental role, which makes it difficult or unfeasible to substantially change these plantations or even eliminate them entirely. Particularly notable in this respect is olive cultivation in the Mediterranean basin, where 97% of world production is concentrated. More specifically, in Spain, which is home to more than 50% of world olive oil production (AICA, 2014), there are 2,420,000 ha of olive crop, and traditional plantations RESEARCH ARTICLE OPEN ACCESS AbstractThe fruit harvesting is a key factor involving both product quality and profitability. Particularly, mechanical harvesting of traditional oil olive orchards is hint by tree training system for manual harvesting, tree size and several and slanted trunks which makes difficult trunk shaker work. Therefore, canopy shaker technology could be a feasible alternative to develop an integral harvester able to work on irregular canopies. The aim of this research was to determine vibration parameters applied to the olive tree for efficient mechanical harvesting by canopy shaker measuring fruit removal efficiency and debris. In this work, a continuous lateral canopy shaker harvester has been developed and tested on large olive trees in order to analyse the operating harvester parameters and tree properties to improve mutual adaptation. Vibration amplitude and frequency, rod density and ground speed were assessed. Vibration amplitude and frequency beside ground speed were decisive factors on fruit removal efficiency. Increasing rod density has not influenced on removal efficiency although it increased significantly debris. Promising results has been reached with 77.3% of removal efficiency, applying a 28 s shaking duration, 0.17 m amplitude vibration and 12 rod drum. This result was obtained reporting 0.26 s of accumulative shaking time over 200 m/s 2 resultant acceleration. The canopy shaker mechanism enabled more than 65% of detached fruits to fall vertically, facilitating catch fruit. In order to improve removal efficiency it is advisable to adapt trees, set high amplitude in the shaker mechanism, and enhance the contact time between rods and tree.Additional key words: Olea europaea L.; fruit detachment; integral harvester; frequency; amplitude; removal efficiency.

<p>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 (<em>Olea europea</em> 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 (&lt;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.</p>

The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 'Salustiana' variety orange trees-Citrus sinensis (L.) Osbeck-(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R 2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate.In the literature, diverse works of detection in different types of fruits or harvest can be found, such as almond [2], apple [3][4][5][6][7], cherryfruit [8], cucumber [9], mango [10,11], orange [12,13], pineapple [14,15], or tomato [16].Fruit detection requires segmentation, shape selection, and identification phases [17]. Segmentation consists of filtering through a colour threshold of the components of the scene that can be considered fruit [18]. Different characteristics of perimeter, area, or compaction allow to select the shapes (blobs) and to identify the fruits one by one.In bidimensional models, circles can be detected by the Hough transform [19] or by adjusting circular contours [20]. The colour cameras allowed Harrell et al. [12] to implement a robotic orange harvesting system and Grasso and Recce [21] to perform RGB segmentation. Qureshi et al.[11] used clustering of K-nearest neighbour pixels. Qureshi et al. [22] presented a texture-based method for shape recognition using an over-segmentation of super-pixels from the local gradient calculation.Colour segmentation using charge coupled device cameras returns pixels with RGB graduations or colour composition by addition of the primary colours red, green, and blue, allowing rapid detection of ripe fruit [23]. However, its drawback is its false positives. In addition, shape detection has a high computational cost. Therefore, a suitable alternative is the use of a colour filter followed by shape detection to avoid these false positives.The colour and shape characteristics allow us to approach its count using 2D photos, filtering by colour or chromaticity, delimiting shapes by contour, and being...

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The introduction of a mechanical harvesting process for oranges can contribute to enhancing farm profitability and reducing labour dependency. The objective of this work is to determine the spread of the vibration in citrus tree canopies to establish recommendations to reach high values of fruit detachment efficiency and eliminate the need for subsequent hand-harvesting processes. Field tests were carried out with a lateral tractor-drawn canopy shaker on four commercial plots of sweet oranges. Canopy vibration during the harvesting process was measured with a set of triaxial accelerometer sensors with a datalogger placed on 90 bearing branches. Monitoring of the vibration process, fruit production, and branch properties were analysed. The improvement of fruit detachment efficiency was possible if both the hedge tree and the machinery were mutually adjusted. The hedge should be trained to facilitate access of the rods and to encourage external fructification since the internal canopy branches showed 43% of the acceleration vibration level of the external branches. The machine should be adjusted to vibrate the branches at a vibration time of at least 5.8 s, after the interaction of the rod with the branch, together with a root mean square acceleration value of 23.9 m/s2 to a complete process of fruit detachment.