Leaf cuticular wax composition of a genetically diverse collection of lettuce (Lactuca sativa L.) cultivars evaluated under field conditions.

Cuticular waxes of plants impart tolerance to many forms of environmental stress and help shed dangerous human pathogens on edible plant parts. Although the chemical composition of waxes on a wide variety of important crops has been described, a detailed wax compositional analysis has yet to be reported for lettuce (Lactuca sativa L.), one of the most widely consumed vegetables. We present herein the leaf wax content and composition of 12 genetically diverse lettuce cultivars sampled across five time points during their vegetative growth phase in the field. Mean total leaf wax amounts across all cultivars varied little over 28 days of vegetative growth, except for a notable decrease in total waxes following a major precipitation event, presumably due to wax degradation from wind and rain. All lettuce cultivars were found to contain a unique wax composition highly enriched in 22- and 24-carbon length 1-alcohols (docosanol and tetracosanol, respectively). In our report, the dominance of these shorter chain length 1-alcohols as wax constituents represents a relatively rare phenotype in plants. The ecological significance of these dominant and relatively short 1-alcohols is still unknown. Although waxes have been a target for improvement of various crops, no such work has been reported for lettuce. This study lays the groundwork for future research that aims to integrate cuticular wax characteristics of field grown plants into the larger context of lettuce breeding and cultivar development.

PhytoOracle: Scalable, modular phenomics data processing pipelines.

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture.

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TRSWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm3 cm-3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.