An automatic trunk-detection system for intensive olive harvesting with trunk shaker

Abstract

Trunk shakers are widely used for olive harvesting, being the main detachment system for fruit harvesting. In recent decades, the components of trunk shakers have evolved at mechanical, hydraulic and control levels. However, machine accuracy depends on the operator, whose expertise is a key factor for issues such as trunk debarking caused by grabbing systems, shaking parameters, or on-foot operator safety. The objective of this work was to develop an automatic trunk-detection system to reduce operator influence on the process. Thus, the automatic system via infrared sensor was implemented on a trunk shaker head hitched to a tractor. A control algorithm, control logic and display for trunk grabbing automation were developed. The automatic system was tested under laboratory and field conditions to assess the influence of some variables on trunk detection. The evaluated variables were colour, material, diameter, and target location within the sensor field of vision. The success rate of the automatic system was 91% for trunk grabbing. In the field phase, the efficacy of the automatic system was compared to an operator performing the tasks manually, obtaining times of 16.05 ± 2.8 s tree−1 and 21.54 ± 5.29 s tree−1 respectively, and a percentage of success in trunk grabbing of 92.9%. Automatic mode improved manual mode by saving 27.3% of time, improving effective field capacity. The automatic mode developed here provided a high ratio of success and it showed highly reliable and efficient performance compared with manual mode.