Special Issue

Intelligent Service Robotics

, Volume 3, Issue 4, pp 245-262

Comprehensive Automation for Specialty Crops: Year 1 results and lessons learned

  • Sanjiv SinghAffiliated withCarnegie Mellon University
  • , Marcel BergermanAffiliated withCarnegie Mellon University Email author 
  • , Jillian CannonsAffiliated withVision Robotics
  • , Benjamin GrocholskyAffiliated withCarnegie Mellon University
  • , Bradley HamnerAffiliated withCarnegie Mellon University
  • , German HolguinAffiliated withPurdue University
  • , Larry HullAffiliated withPennsylvania State University
  • , Vincent JonesAffiliated withWashington State University
  • , George KantorAffiliated withCarnegie Mellon University
    • , Harvey KoselkaAffiliated withVision Robotics
    • , Guiqin LiAffiliated withPurdue University
    • , James OwenAffiliated withOregon State University
    • , Johnny ParkAffiliated withPurdue University
    • , Wenfan ShiAffiliated withCarnegie Mellon University
    • , James TezaAffiliated withCarnegie Mellon University

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Abstract

Comprehensive Automation for Specialty Crops is a project focused on the needs of the specialty crops sector, with a focus on apples and nursery trees. The project’s main thrusts are the integration of robotics technology and plant science; understanding and overcoming socio-economic barriers to technology adoption; and making the results available to growers and stakeholders through a nationwide outreach program. In this article, we present the results obtained and lessons learned in the first year of the project with a reconfigurable mobility infrastructure for autonomous farm driving. We then present sensor systems developed to enable three real-world agricultural applications—insect monitoring, crop load scouting, and caliper measurement—and discuss how they can be deployed autonomously to yield increased production efficiency and reduced labor costs.

Keywords

Specialty crops Reconfigurable mobility Crop intelligence Insect monitoring Crop load estimation Caliper measurement