Evaluation of Over-The-Row Harvester Damage in a Super-High-Density Olive Orchard Using On-Board Sensing Techniques

Abstract

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.

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

“…Leaf area and parameters related to leaf area can be calculated from the 3D images of plants obtained by the lidar [20][21][22][23][24]. Other than the parameters above, tree volume and species can be estimated [25][26][27]. The detailed and accurate information from lidar provides a useful tool, for example, in the field of plant phenotyping [28].…”

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System

“…In further studies, a higher computer performance is needed, since the data acquisition system was sometimes overloaded, resulting in scan loss; hence, some areas remained undersampled. This is due to the large datasets acquired by LiDAR sensors causing long processing times [19]. In addition, a R-squared value of 0.85 was found for the arithmetic mean of the number of scans and volumes.…”

“…Depth data was collected by setting the field of view to 270 • at 0.5 • angular steps and a sampling frequency of 50 Hz (every 20 ms). LiDAR readings were acquired and processed by a developed LabVIEW programme (National Instruments Co., Austin, TX, USA) [19]. Height profiles of the vines were obtained by distance extraction, generating a high-density point cloud containing all points where the laser beam impacted.…”

“…To assess improvements in the operating conditions of harvesters in order to minimize fruit damage in high-density olive groves. Pérez-Ruiz, Rallo [19] attached two LiDAR sensing devices to the front of a tractor to estimate volume and biomass losses during harvesting. Ref.…”

“…Sensors 2019,19 FOR PEERREVIEW 11 Intercepts and estimates of the linear regression analysis by treatment with their corresponding standard error (SE) and significance levels (p-value). Significance codes: <0.001 '***'; <0.01 '**'; < 0.05 '*'; > 0.05 'ns'.…”