4.7 Article

Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping

期刊

AGRONOMY-BASEL
卷 4, 期 2, 页码 279-301

出版社

MDPI
DOI: 10.3390/agronomy4020279

关键词

UAV; UAS; plant breeding; remote sensing; canopy temperature; crop establishment; lodging; wheat; sorghum; sugarcane

资金

  1. CSIRO
  2. Divisions of Plant Industry and of Maths and Information Sciences
  3. Climate Adaptation Flagship

向作者/读者索取更多资源

Plant breeding trials are extensive (100s to 1000s of plots) and are difficult and expensive to monitor by conventional means, especially where measurements are time-sensitive. For example, in a land-based measure of canopy temperature (hand-held infrared thermometer at two to 10 plots per minute), the atmospheric conditions may change greatly during the time of measurement. Such sensors measure small spot samples (2 to 50 cm(2)), whereas image-based methods allow the sampling of entire plots (2 to 30 m(2)). Capturing images from an aircraft which is flown precisely at low altitude (10 to 40 m) to obtain high ground resolution data for every plot allows the rapid measurement of large numbers of plots. This paper outlines the implementation of a customized robotic helicopter (gas-powered, 1.78-m rotor diameter) with autonomous flight control and software to plan flights over experiments that were 0.5 to 3 ha in area and, then, to extract, straighten and characterize multiple experimental field plots from images taken by three cameras. With a capacity to carry 1.5 kg for 30 min or 1.1 kg for 60 min, the system successfully completed >150 flights for a total duration of 40 h. Example applications presented here are estimations of the variation in: ground cover in sorghum (early season); canopy temperature in sugarcane (mid-season); and three-dimensional measures of crop lodging in wheat (late season). Together with this hardware platform, improved software to automate the production of ortho-mosaics and digital elevation models and to extract plot data would further benefit the development of high-throughput field-based phenotyping systems.

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