Optimization Model for Selective Harvest Planning Performed by Humans and Robots

Ben Harel, Yael Edan, Yael Perlman

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number2507
JournalApplied Sciences (Switzerland)
Issue number5
StatePublished - 1 Mar 2022

Bibliographical note

Funding Information:
Funding: Partially funded by the European Commission (SWEEPER GA No. 66313) and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological, and Cognitive Robotics Initiative, the Marcus Endowment Fund, and the Rabbi W. Gunther Plaut in Manufacturing Engineering.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • Agriculture
  • Harvest planning
  • Nonlinear programming
  • Production


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