NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research

Geng Bai, Yufeng Ge, David Scoby, Bryan Leavitt, Vincent Stoerger, Norbert Kirchgessner, Suat Irmak, George Graef, James Schnable, Tala Awada

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Field-based high throughput plant phenotyping has recently gained increased interest in the efforts to bridge the genotyping and phenotyping gap and accelerate plant breeding for crop improvement. In this paper, we introduce a large-scale, integrated robotic cable-driven sensing system developed at University of Nebraska for field phenotyping research. It is constructed to collect data from a 0.4 ha field. The system has a sensor payload of 30 kg and offers the flexibility to integrate user defined sensing modules. Currently it integrates a four-band multispectral camera, a thermal infrared camera, a 3D scanning LiDAR, and a portable visible near-infrared spectrometer for plant measurements. Software is designed and developed for instrument control, task planning, and motion control, which enables precise and flexible phenotypic data collection at the plot level. The system also includes a variable-rate subsurface drip irrigation to control water application rates, and an automated weather station to log environmental variables. The system has been in operation for the 2017 and 2018 growing seasons. We demonstrate that the system is reliable and robust, and that fully automated data collection is feasible. Sensor and image data are of high quality in comparison to the ground truth measurements, and capture various aspects of plant traits such as height, ground cover and spectral reflectance. We present two novel datasets enabled by the system, including a plot-level thermal infrared image time-series during a day, and the signal of solar induced chlorophyll fluorescence from canopy reflectance. It is anticipated that the availability of this automated phenotyping system will benefit research in field phenotyping, remote sensing, agronomy, and related disciplines.

Original languageEnglish (US)
Pages (from-to)71-81
Number of pages11
JournalComputers and Electronics in Agriculture
Volume160
DOIs
StatePublished - May 2019

All Science Journal Classification (ASJC) codes

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

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