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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. [ABSTRACT FROM AUTHOR]

The development of novel robotic harvesters could benefit significantly from a model-based design approach, in which harvesting performance metrics—such as fruit reachability and average pick-and-place cycle—are calculated via simulation, and are used to guide mechanical design. The actual spatial distributions of fruits on orchard trees are necessary for such an approach. Reported methods for measuring the locations of all fruits require several minutes per fruit, and, consequently, have been used only for very small numbers of trees. The novel method presented utilizes high-frequency radio signals and trilateration to measure the locations of all fruits in canopies, at speeds that are significantly higher than those of existing methods. More specifically, a fruit picker wears gloves on which an antenna has been attached. A mobile trailer carries four radio beacons that measure and log their distances from the antenna on each glove, every time a fruit is grasped to be picked. The coordinates of each glove are computed with respect to a coordinate frame attached to the trailer, and the fruit position is approximated by these coordinates. Data from an RTK-GPS and an inclinometer on the trailer are used to compute georeferenced fruit coordinates. Data were collected for 32,193 fruits in eight California pear and cling peach orchards. The measurement rate varied from approximately 8–12 fruits per minute, with an average of 10.8, which is a magnitude faster than existing reported methods. In open space, the root mean square error between the estimated and true distance (DRMS) in the system’s measurement volume was measured to be 10.3 cm. The error’s 90th percentile (R90) was 13.1 cm. In the periphery of and inside canopies, these errors were calculated via Monte Carlo simulation to be equal to 15.7 cm and 24.9 cm respectively. The horizontal accuracies (across and along the row), and the vertical accuracy were 9.6 cm, 4.3 cm and 5.7 cm respectively. The corresponding worst-case relative accuracies were 2.7%, 1.6%, and 3.4%, and were calculated by dividing each accuracy component by the distance between the fruits that were as far away as possible from each other along the corresponding axis. Finally, fruit position statistics, such as fruit elevation and horizontal distance from the row centers were computed and reported for a set of pear trees. Such data can be very useful for growers and for model-based design of harvesting machinery. [ABSTRACT FROM AUTHOR]