UC Davis

Harvest-aid robots that transport empty and full trays during manual harvesting of specialty crops such as strawberries or table grapes can increase harvest efficiency, by reducing pickers' non-productive walking times. In Part I of this work, a modeling framework, and a stochastic simulator were presented for all-manual and robot-aided harvesting. This paper reports Part II of our work, which utilized data gathered in two strawberry fields during harvesting, to estimate the stochastic parameters involved in modeling pickers, and evaluate the prediction accuracy of the simulator for all-manual picking. Then, as a case study, non-productive time and harvest efficiency were estimated for robot-aided harvesting, for various picker-robot ratios and three priority-based reactive dispatching strategies for the robots. The simulator predicted the pickers' non-productive time during all-manual harvesting, with 6.4%, 3%, and 1.2% errors for the morning, afternoon, and “all-day” harvesting shifts, respectively. Statistical testing verified that predicted non-productive times followed the same distributions as the measured non-productive times (5% significance level). Simulations robustness was assessed by using morning data to simulate afternoon harvesting and vice-versa: non-productive times distributions were predicted accurately (10% significance level). Robot-aided simulation results – using the calibrated simulator for a 25-picker crew – showed that all-manual harvest efficiencies of 81.8% and 78.2% for morning and afternoon shifts increased to 92% and 86.5%, respectively, when five robots were deployed. Different scheduling policies did not affect efficiency when more than five robots were used, because there were always enough robots to serve pickers' requests immediately. Also, harvest efficiency plateaued when more than five robots were used, as a consequence of the time needed for a robot to travel to a picker.