TY - JOUR
T1 - NU-Spidercam
T2 - A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research
AU - Bai, Geng
AU - Ge, Yufeng
AU - Scoby, David
AU - Leavitt, Bryan
AU - Stoerger, Vincent
AU - Kirchgessner, Norbert
AU - Irmak, Suat
AU - Graef, George
AU - Schnable, James
AU - Awada, Tala
N1 - Funding Information:
The funding for this work was provided by (1) University of Nebraska-Lincoln , (2) the Hatch Act capacity funding program (accession# 1011130 ) of USDA-NIFA , (3) National Science Foundation ( DBI-1556186 ), (4) Nebraska Soybean Board , and (5) Nebraska Corn Board . The authors would like to thank Nathan Duffy for his long-hour assistance in the field to support the operation of NU-Spidercam; and graduate students Ali Mohammed, Shawn Jenkins, and Preston Hurst for collecting the ground truth data. Dr. Hector Santiago worked closely with the team to address many logistic challenges. Dr. Arthur I. Zygielbaum made the selection of the plant sensors mounted on NU-Spidercam. The authors would also like to thank the technical support team of Spidercam (Spidercam GmbH, Austria) working with us for this enjoyable and successful project.
Publisher Copyright:
© 2019 The Authors
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - Image analysis
KW - LiDAR
KW - Multispectral imagery
KW - Reflectance spectra
KW - Thermal infrared imagery
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U2 - 10.1016/j.compag.2019.03.009
DO - 10.1016/j.compag.2019.03.009
M3 - Article
AN - SCOPUS:85062832872
SN - 0168-1699
VL - 160
SP - 71
EP - 81
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
ER -