In-field grape cluster size assessment for vine yield estimation using a mobile robot and a consumer level RGB-D Camera

Polina Kurtser, Ola Ringdahl, Nati Rotstein, Ron Berenstein, Yael Edan

Research output: Contribution to journalArticlepeer-review


Current practice for vine yield estimation is based on RGB cameras and has limited performance. In this letter we present a method for outdoor vine yield estimation using a consumer grade RGB-D camera mounted on a mobile robotic platform. An algorithm for automatic grape cluster size estimation using depth information is evaluated both in controlled outdoor conditions and in commercial vineyard conditions. Ten video scans (3 camera viewpoints with 2 different backgrounds and 2 natural light conditions), acquired from a controlled outdoor experiment and a commercial vineyard setup, are used for analyses. The collected dataset (GRAPES3D) is released to the public. A total of 4542 regions of 49 grape clusters were manually labeled by a human annotator for comparison. Eight variations of the algorithm are assessed, both for manually labeled and auto-detected regions. The effect of viewpoint, presence of an artificial background, and the human annotator are analyzed using statistical tools. Results show 2.8-3.5 cm average error for all acquired data and reveal the potential of using low-cost commercial RGB-D cameras for improved robotic yield estimation.

Original languageAmerican English
Article number8977358
Pages (from-to)2031-2038
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - 1 Apr 2020


  • Field robots
  • RGB-D perception
  • agricultural automation
  • robotics in agriculture and forestry

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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