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Proceedings Paper

Cotton phenotyping with lidar from a track-mounted platform
Author(s): Andrew N. French; Michael A. Gore; Alison Thompson
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Paper Abstract

High-Throughput Phenotyping (HTP) is a discipline for rapidly identifying plant architectural and physiological responses to environmental factors such as heat and water stress. Experiments conducted since 2010 at Maricopa, Arizona with a three-fold sensor group, including thermal infrared radiometers, active visible/near infrared reflectance sensors, and acoustic plant height sensors, have shown the validity of HTP with a tractor-based system. However, results from these experiments also show that accuracy of plant phenotyping is limited by the system’s inability to discriminate plant components and their local environmental conditions. This limitation may be overcome with plant imaging and laser scanning which can help map details in plant architecture and sunlit/shaded leaves. To test the capability for mapping cotton plants with a laser system, a track-mounted platform was deployed in 2015 over a full canopy and defoliated cotton crop consisting of a scanning LIDAR driven by Arduinocontrolled stepper motors. Using custom Python and Tkinter code, the platform moved autonomously along a pipe-track at 0.1 m/s while collecting LIDAR scans at 25 Hz (0.1667 deg. beam). These tests showed that an autonomous LIDAR platform can reduce HTP logistical problems and provide the capability to accurately map cotton plants and cotton bolls.

A prototype track-mounted platform was developed to test the use of LIDAR scanning for High- Throughput Phenotyping (HTP). The platform was deployed in 2015 at Maricopa, Arizona over a senescent cotton crop. Using custom Python and Tkinter code, the platform moved autonomously along a pipe-track at <1 m/s while collecting LIDAR scans at 25 Hz (0.1667 deg. beam). Scanning data mapped the canopy heights and widths, and detected cotton bolls.

Paper Details

Date Published: 17 May 2016
PDF: 8 pages
Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 98660B (17 May 2016); doi: 10.1117/12.2224423
Show Author Affiliations
Andrew N. French, USDA/ARS (United States)
Michael A. Gore, Cornell Univ. (United States)
Alison Thompson, USDA/ARS (United States)


Published in SPIE Proceedings Vol. 9866:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
John Valasek; J. Alex Thomasson, Editor(s)

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