Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For field-based high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.

Plant height is an important morphological and developmental phenotype that directly indicates overall plant growth and is widely predictive of final grain yield and biomass. Currently, manually measuring plant height is laborious and has become a bottleneck for genetics and breeding programs. The goal of this research was to evaluate the performance of five different sensing technologies for field-based high throughput plant phenotyping (HTPP) of sorghum [Sorghum bicolor (L.) Moench] height. With this purpose, (1) an ultrasonic sensor, (2) a LIDAR-Lite v2 sensor, (3) a Kinect v2 camera, (4) an imaging array of four high-resolution cameras were evaluated on a ground vehicle platform, and (5) a digital camera was evaluated on an unmanned aerial vehicle platform to obtain the performance baselines to measure the plant height in the field. Plot-level height was extracted by averaging different percentiles of elevation observations within each plot. Measurements were taken on 80 single-row plots of a US × Chinese sorghum recombinant inbred line population. The performance of each sensing technology was also qualitatively evaluated through comparison of device cost, measurement resolution, and ease and efficiency of data analysis. We found the heights measured by the ultrasonic sensor, the LIDAR-Lite v2 sensor, the Kinect v2 camera, and the imaging array had high correlation with the manual measurements (r ≥ 0.90), while the heights measured by remote imaging had good, but relatively lower correlation to the manual measurements (r = 0.73). These results confirmed the ability of the proposed methodologies for accurate and efficient HTPP of plant height and can be extended to a range of crops. The evaluation approach discussed here can guide the field-based HTPP research in general.

To maintain high yields under an increasingly hotter climate, high temperature resilient peanut cultivars would have to be developed. Therefore, the mechanisms of plant response to heat need to be understood. The objective of this study was to explore the physiological and metabolic mechanisms developed by virginia-type peanut at early growth stages in response to high temperature stress. Peanut seedlings were exposed to 40/35°C (heat) and 30/25°C (optimum temperature) in a growth chamber. Membrane injury (MI), the Fv/Fm ratio, and several metabolites were evaluated in eight genotypes at four time-points (day 1, 2, 4, and 7) after the heat stress treatment initiation. Even though we were able to highlight some metabolites, e.g., hydroxyproline, galactinol, and unsaturated fatty acid, explaining specific differential physiological (MI) responses in peanut seedlings, overall our data suggested general stress responses rather than adaptive mechanisms to heat. Rather than individual metabolites, a combination of several metabolites better explained (41 to 61%) the MI variation in heat stressed peanut seedlings. The genotype SPT 06 07 exhibited lower MI, increased galactinol, reduced hydroxyproline, and higher saturated vs. unsaturated fatty acid ratio under heat stress compared to other genotypes. SPT 06-07 was also separated from the other genotypes during hierarchical clustering and, based on this and previous fieldwork, SPT 06-07 is proposed as a potential source for heat tolerance improvement of virginia-type peanut.