RTK GPS mapping of transplanted row crops

  • Authors:

  • H. Sun;D. C. Slaughter;M. Pérez Ruiz;C. Gliever;S. K. Upadhyaya;R. F. Smith

  • Affiliations:

  • University of California, Davis, Department of Biological and Agricultural Engineering, United States;University of California, Davis, Department of Biological and Agricultural Engineering, United States;Universidad de Sevilla, Área de Ingeniería Agroforestal, Dpto. de Ingeniería Aeroespacial y Mecánica de Fluidos, Spain;University of California, Davis, Department of Biological and Agricultural Engineering, United States;University of California, Davis, Department of Biological and Agricultural Engineering, United States;University of California, Davis, Department of Plant Sciences, United States

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2010

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Abstract

This study demonstrated the feasibility of using a real-time kinematic (RTK) global positioning system (GPS) to automatically map the location of transplanted row crops. A positive-placement vegetable crop transplanter retrofitted with an RTK GPS receiver, plant, inclination, and odometry sensors, and an on-board real-time data logger were used for transplant mapping in the field during planting. Sensing the location where each plant was placed in the soil using an absolute shaft encoder mounted on the planting wheel proved to be more robust and accurate than using an infrared light beam sensor to detect the stem location of each plant immediately after planting. Field test results showed that the mean error between the plant map locations predicted by the planting data and the surveyed locations after planting was 2cm, with 95% of the predicted plant locations being within 5.1cm of their actual locations. Along-track errors were greater than transverse-track errors indicating that some improvement in plant map accuracy might be obtained by characterization of dynamic planting effects on final plant location. Overall, the system was capable of automatically producing a centimeter-level accuracy plant map suitable for use in precision plant care tasks such as intra-row weed control.