Bhadra, Sourav, Vasit Sagan, Maitiniyazi Maimaitijiang, Matthew Maimaitiyiming, Maria Newcomb, Nadia Shakoor, and Todd C. Mockler. "Quantifying leaf chlorophyll concentration of sorghum from hyperspectral data using derivative calculus and machine learning." Remote Sensing 12, no. 13 (2020): 2082. https://www.mdpi.com/2072-4292/12/13/2082

Herritt et al 2020 demonstrate the PSII camera's ability to capture PSII fluorescence by treating leaves with a chemical that inhibits photosynthesis (DMCU) and observing differences.   

Herritt, M.T., Pauli, D., Mockler, T.C. et al. Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting. Plant Methods 16, 109 (2020). https://doi.org/10.1186/s13007-020-00650-0

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and more accessibility of both UAVs and thermal imaging sensors, has led to the widespread usage of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market becoming more available at a low cost. However, their efficacy and accuracy in various applications has not been tested.

High-throughput phenotyping platforms (HTPPs) provide novel opportunities to more effectively dissect the genetic basis of drought-adaptive traits. This genome-wide association study (GWAS) compares the results obtained with two Unmanned Aerial Vehicles (UAVs) and a ground-based platform used to measure Normalized Difference Vegetation Index (NDVI) in a panel of 248 elite durum wheat (Triticum turgidum L. ssp. durum Desf.) accessions at different growth stages and water regimes.

The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center.