Optimization of agrochemical application in olive groves based on positioning sensor

Optimization of agrochemical application in olive groves based on positioning sensor Typically, agrochemicals are applied to olive trees uniformly within a whole orchard without regard to the spatial variability of the target tree profile. The treatment efficiency can be improved by reducing the spray losses associated with deposition on the ground and off-target drift. The goals of this study were to develop a technique to evaluate the chemical losses resulting from a spray treatment applied to Spanish olive trees and to design an automated control system to adjust the application volume based on tree structure information incorporated in a prescription map and GPS technology. The automatic control system selectively actuated individual sections of the spray boom in real-time based upon the prescribed demand in the GPS map and the current geo-position information provided by the tractor’s RTK GPS system. Test results indicated that the control system, mounted on a conventional sprayer, was able to reduce the volume of spray application on the six tree rows tested by 19% when compared to conventional spray application techniques. Results also indicated that the average application loss to the soil, for all the trees, was reduced 15.25% compared to treatment with conventional equipment. Two spray collection masts of 9.5 m height were designed and built to measure the drift produced during spray application and therefore to provide data for analysis of spray distribution at various application heights within the tree. Results show that the new control system was able to achieve the same spray distribution on the tree as the conventional sprayer while reducing the volume of chemical applied. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Optimization of agrochemical application in olive groves based on positioning sensor

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Springer US
Copyright © 2010 by The Author(s)
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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