Anaheim Convention Center
Anaheim, California, United States
26 - 30 April 2020
Conference SI211
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
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Abstract Due:
16 October 2019

Author Notification:
20 December 2019

Manuscript Due Date:
1 April 2020

The Climate Corporation
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Conference Chairs
Program Committee
  • Christoph Bauer, KWS SAAT AG (Germany)
  • Subodh Bhandari, California State Polytechnic Univ., Pomona (United States)
  • Andrew N. French, Agricultural Research Service (United States)
  • Yufeng Ge, Univ. of Nebraska-Lincoln (United States)
  • Seth C. Murray, Texas A&M Univ. (United States)

Program Committee continued...
  • Haly Neely, Texas A&M Univ. (United States)
  • Boyan Peshlov, McCain Foods (United States)
  • Carl Salvaggio, Rochester Institute of Technology (United States)
  • Michael Sama, Univ. of Kentucky (United States)
  • Sindhuja Sankaran, Washington State Univ. (United States)
  • Ajay Sharda, Kansas State Univ. (United States)
  • Yeyin Shi, Univ. of Nebraska-Lincoln (United States)

Call for
The use of photonics technologies in agriculture is a rapidly emerging and promising area of study, given the potential impact these technologies offer for rapid crop improvement through breeding and genetics as well optimization of on-farm crop production. The field is in an exciting period of exploration and expansion, as the use of ground- and air-based sensor platforms now permits revolutionizing measurement of plant traits by adding great detail, high throughput, and concomitantly large data volumes. This conference brings together researchers and practitioners in this field to discuss the latest technologies, methods and findings.

Proximal and remote sensing systems including point and array detectors and automated ground-based and aerial vehicles applied to agriculture and high-throughput phenotyping are within the scope of this conference. Both active and passive sensing methods as well as sensors based on material reflectance and transmission and such physical phenomena as fluorescence and Raman scattering are pertinent to this conference. Optical sensing extending from the UV through the IR where thermal imaging becomes an important methodology is yet another area of active research of interest.

This conference will place emphasis on the use of unmanned aerial vehicles (UAVs) and ground-based robotic platforms equipped with various sensing technologies for the purpose of plant and crop phenotyping studies as applied to improving crop characteristics including yield, drought tolerance, stress detection, etc.. Contributions are sought on sensing technologies; sensor platforms; and data collection, analysis and visualization schemes. Contributions are welcome which contain results from field studies on topics such as, but not limited to:
  • UAVs for remote sensing in agriculture, including autonomous control issues, imaging workflow issues, and imaging software issues
  • Ground-based robots for phenotyping
  • Hyperspectral imaging
  • Multispectral imaging
  • Lidar
  • Thermal-infrared cameras
  • Fluorescence cameras
  • Mobile Raman spectrometers
  • Image analysis, data management and data visualization
  • Theoretical and empirical estimation techniques including machine learning
BEST PAPER AWARDS: The Conference Chair and Program Committee would like to recognize pioneers in the field with a Best Paper Award. Two candidates will be selected: one winner for the Best Paper Award and a Runner-up. This award is open to all authors who present in this conference.

2019 Best Paper Award Winners
Award Winner
The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T) [11008-3]. Given to: Mahyar Aboutalebi, Alfonso F. Torres-Rua, Mac McKee, Calvin Coopmans, Utah State Univ. (United States), Hector Nieto, Complutum Tecnologas de la Informacin Geogrfica (Spain), William Kustas, U.S. Dept. of Agriculture (United States).

An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes wine country of upstate New York [11008-35]. Rinaldo R. Izzo, Evan D. Marcellus, Timothy D. Bauch, Nina G. Raqueño, Jan van Aardt, Rochester Institute of Technology (United States), Alan N. Lakso, Cornel Univ. (United States)
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