Evaluation of Over-The-Row Harvester Damage in a Super-High-Density Olive Orchard Using On-Board Sensing Techniques
|Autor||Pérez Ruiz, Manuel
Rallo Morillo, Pilar
Jiménez González, María Rocío
Garrido Izard, Miguel
Suárez García, María Paz
Casanova Lerma, Laura
Martínez Guanter, Jorge
Morales Sillero, Ana María
|Departamento||Universidad de Sevilla. Departamento de Ciencias Agroforestales
Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos
|Publicado en||Sensors, 2018 (18(4) 1242), 1-16.|
|Tipo de documento||Artículo|
|Resumen||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 ...
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.
|Cita||Pérez Ruiz, M., Rallo Morillo, P., Jiménez González, M.R., Garrido Izard, M., Suárez García, M.P., Casanova Lerma, L.,...,Morales Sillero, A.M. (2018). Evaluation of Over-The-Row Harvester Damage in a Super-High-Density Olive Orchard Using On-Board Sensing Techniques. Sensors, 2018 (18(4) 1242), 1-16.|