Abstract
This paper addresses the formulation of an individual fruit harvest decision as a nonlinear programming problem to maximize profit, while considering selective harvesting based on fruit maturity. A model for the operational level decision was developed and includes four features: time window constraints, resource limitations, yield perishability, and uncertainty. The model implementation was demonstrated through numerical studies that compared decisions for different types of worker and analyzed different robotic harvester capabilities for a case study of sweet pepper harvesting. The results show the influence of the maturity classification capabilities of the robot on its output, as well as the improvement in cycle times needed to reach the economic feasibility of a robotic harvester.
Highlights
The supply chain for fresh fruit and vegetables involves a number of steps from crop selection to shipment to the customer, including harvesting, processing, packaging, and transporting the produce [1]
The current paper addresses this knowledge gap by focusing on decisions related to individual fruit and taking into consideration four features for selective harvesting
The results show the importance of the robot harvest capabilities in one period and the improvement in cycle times needed for a robotic harvester to reach economic feasibility
Summary
The supply chain for fresh fruit and vegetables involves a number of steps from crop selection to shipment to the customer, including harvesting, processing, packaging, and transporting the produce [1]. The third and last aspect investigates the operational considerations of maturity classification and harvesting, namely, deciding whether the fruit is ripe and whether to harvest it, based on a consideration of the above points By addressing these aspects, the harvest decision of a selective robotic harvester can be improved. We aimed to develop a model for selective harvesting based on the maturity of the fruit, where harvesting can be performed by human workers or robots, with each type of harvester having different abilities to identify maturity. A robotic harvester will be able to non-destructively classify the exact maturity level [24,36–39] and will have a higher harvest capacity than a human worker, but will have higher costs [23].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
