Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (DS)

The use of optics and photonics in agriculture is a rapidly advancing and promising area of study, given the potential impact these technologies offer for rapid crop improvement through phenomics research as well optimization of on-farm crop production. This field is in an exciting period of exploration and expansion, as the use of ground- and air-based sensor platforms now permits revolutionizing the 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 deployed on automated ground-based and aerial vehicles in the context of production agriculture and/or phenomics are within the scope of this conference. Both active and passive sensing methods as well as sensors based on various light-scattering modes including fluorescence and Raman are pertinent. Optical sensing, extending from the UV through the IR where thermal imaging becomes an important methodology, is included in the area of interest.

This conference will emphasize ground-based robots and unmanned aerial vehicles (UAVs) equipped with sensors for measuring phenotypes such as yield, drought tolerance, various stresses, etc. Contributions are sought on sensing technologies; sensor platforms; and data collection, analysis and visualization schemes. While not an exhaustive list, the following exemplifies the technologies and methodologies of interest:
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 and artificial intelligence.