SpykProps: an imaging pipeline to quantify architecture in unilateral grass inflorescences.

BACKGROUND: Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. RESULTS: Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE < 1), estimate their length (R2 = 0.96), and number of spikelets (R2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R2 = 0.94), rachis weight (R2 = 0.83), and seed shattering (R2 = 0.85). CONCLUSIONS: SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.

A latent scale model to minimize subjectivity in the analysis of visual rating data for the National Turfgrass Evaluation Program.

Introduction: Traditional evaluation procedure in National Turfgrass Evaluation Program (NTEP) relies on visually assessing replicated turf plots at multiple testing locations. This process yields ordinal data; however, statistical models that falsely assume these to be interval or ratio data have almost exclusively been applied in the subsequent analysis. This practice raises concerns about procedural subjectivity, preventing objective comparisons of cultivars across different test locations. It may also lead to serious errors, such as increased false alarms, failures to detect effects, and even inversions of differences among groups. Methods: We reviewed this problem, identified sources of subjectivity, and presented a model-based approach to minimize subjectivity, allowing objective comparisons of cultivars across different locations and better monitoring of the evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings from the 2017 NTEP Kentucky bluegrass trials at seven testing locations. Results: Compared with the existing method, ours allows the estimation of additional parameters, i.e., category thresholds, rating severity, and within-field spatial variations, and provides better separation of cultivar means and more realistic standard deviations. Discussion: To implement the proposed model, additional information on rater identification, trial layout, rating date is needed. Given the model assumptions, we recommend small trials to reduce rater fatigue. For large trials, ratings can be conducted for each replication on multiple occasions instead of all at once. To minimize subjectivity, multiple raters are required. We also proposed new ideas on temporal analysis, incorporating existing knowledge of turfgrass.

Layering contrasting photoselective filters improves the simulation of foliar shade.

BACKGROUND: Neutral density shade cloth is commonly used for simulating foliar shade, in which it reduces light intensity without altering spectral quality. However, foliar shade also alters spectral quality, reducing the ratio of red to far-red (R:FR) light, altering the ratio of blue to green (B:G) light, and reducing ultraviolet light. Unlike shade cloth, photoselective filters can alter spectral quality, but the filters used in previous literature have not simulated foliar shade well. We examined the spectral quality of sunlight under color temperature blue (CTB), plus green (PG), and neutral density (ND) filters from LEE Filters, Rosco e-colour + and Cinegel brands either alone or layered, hypothesizing that the contrasting filter qualities would improve simulations. As a proof-of-concept, we collected spectral data under foliar shade to compare to data collected under photoselective filters. RESULTS: Under foliar shade reductions in the R:FR ratio ranged from 0.11 to 0.54 (~ 1.18 in full sun), while reductions in the B:G ratio were as low as 0.53 in deep shade, or were as high as 1.11 in moderate shade (~ 0.87 in full sun). Neutral density filters led to near-neutral reductions in photosynthetically active radiation and reduced the R:FR ratio similar to foliar shade. Color temperature blue filters simulated the increased B:G ratio observed under moderate foliar shade, but did not reduce the R:FR ratio low enough. On their own, PG filters did not simulate any type of foliar shade. Different brands of the same filter type also had disparate effects on spectral quality. Layered CTB and ND filters improved the accuracy of moderate foliar shade simulations, and layering CTB, PG, and ND filters led to accurate simulations of deep foliar shade. CONCLUSIONS: Layering photoselective filters with contrasting effects on the spectral quality of sunlight results in more accurate simulations of foliar shade compared to when these filters are used separately. Layered filters can re-create the spectral motifs of moderate and deep foliar shade; they could be used to simulate shade scenarios found in different cropping systems. Photoselective filters offer numerous advantages over neutral density shade cloth and could be a direct replacement for researchers currently using neutral density shade cloth.

Towards Improved Molecular Identification Tools in Fine Fescue (Festuca L., Poaceae) Turfgrasses: Nuclear Genome Size, Ploidy, and Chloroplast Genome Sequencing.

Fine fescues (Festuca L., Poaceae) are turfgrass species that perform well in low-input environments. Based on morphological characteristics, the most commonly-utilized fine fescues are divided into five taxa: three are subspecies within F. rubra L. and the remaining two are treated as species within the F. ovina L. complex. Morphologically, these five taxa are very similar; both identification and classification of fine fescues remain challenging. In an effort to develop identification methods for fescues, we used flow cytometry to estimate genome size and ploidy level and sequenced the chloroplast genome of all five taxa. Fine fescue chloroplast genome sizes ranged from 133,331 to 133,841 bp and contained 113-114 genes. Phylogenetic relationship reconstruction using whole chloroplast genome sequences agreed with previous work based on morphology. Comparative genomics suggested unique repeat signatures for each fine fescue taxon that could potentially be used for marker development for taxon identification.

A novel method to characterize silica bodies in grasses.

BACKGROUND: The deposition of silicon into epidermal cells of grass species is thought to be an important mechanism that plants use as a defense against pests and environmental stresses. There are a number of techniques available to study the size, density and distribution pattern of silica bodies in grass leaves. However, none of those techniques can provide a high-throughput analysis, especially for a great number of samples. RESULTS: We developed a method utilizing the autofluorescence of silica bodies to investigate their size and distribution, along with the number of carbon inclusions within the silica bodies of perennial grass species Koeleria macrantha. Fluorescence images were analyzed by image software Adobe Photoshop CS5 or ImageJ that remarkably facilitated the quantification of silica bodies in the dry ash. We observed three types of silica bodies or silica body related mineral structures. Silica bodies were detected on both abaxial and adaxial epidermis of K. macrantha leaves, although their sizes, density, and distribution patterns were different. No auto-fluorescence was detected from carbon inclusions. CONCLUSIONS: The combination of fluorescence microscopy and image processing software displayed efficient utilization in the identification and quantification of silica bodies in K. macrantha leaf tissues, which should applicable to biological, ecological and geological studies of grasses including forage, turf grasses and cereal crops.