Biology subjects

Schuhl, H.

Publications and source records attributed to Schuhl, H..

5 recordsLinked to original sources

bioRxiv2026

Quantifying growth and lodging in Tef (Eragrostis tef) with Uncrewed Aerial Systems (UAS)

Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance StatementExtreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef (Eragrostis tef). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.

plant biology↗

bioRxiv2026

Smartphone image capture system and image analysis pipelines enable accurate and efficient phenotyping of spaced plant mapping populations

Plain Language SummaryThe lack of low-cost, user-friendly and expedient methods for plant phenotyping challenges researchers ability to efficiently collect accurate phenotypic data in large field experiments. Here, we demonstrate the use of a novel, smartphone-based image capture system and two user-friendly image analysis pipelines (utilizing PlantCV or Biodock AI) to increase the throughput of plant phenotyping in two large, spaced plant populations. We showed that the image capture system collected images of adequate quality for downstream analysis using either the PlantCV or Biodock AI pipeline. Both image analysis pipelines produced phenotype values in line with those obtained using manual image annotation. Together, these results provide researchers with a low-cost, user-friendly image-based phenotyping method that can be widely applied to increase phenotyping throughout in field experiments.

plant biology↗

bioRxiv2025

PlantCV v4: Image analysis software for high-throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEASO_LIPlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. C_LIO_LIPlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. C_LIO_LIThe software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science. C_LI

plant biology↗

bioRxiv2025

Leveraging High-dimensional Seed Colorspace in Phenomic Selection Models for Herbaceous Perennial Crops

Herbaceous perennial crops, which only recently entered the domestication pipeline, offer the potential of ecological benefits in agricultural systems but remain underdeveloped, partly due to slow breeding cycles. Early-stage phenomic selection, the process of using high-dimensional secondary traits to predict target traits, may accelerate improvement. We evaluated whether seed traits derived from image scans could be used to predict germination traits in three perennial crop candidate species: sainfoin (Onobrychis viciifolia), intermediate wheatgrass (Thinopyrum intermedium), and silflower (Silphium integrifolium). Over 20,000 seeds were scanned, and PlantCV was used to extract 692 hue, saturation, and value (HSV) color features along with five morphological traits. We demonstrated that seed color, morphology, and germination traits were all influenced by maternal family, but patterns of correlation between germination traits and color and morphology traits are complex. To enable prediction from high-dimensional seed traits, we constructed relationship matrices from combinations of HSV and morphology features for use in phenomic selection models to predict germination proportion and timing. Model performance varied by trait, species, and cohort, and in many cases, predictions were significantly better than chance. HSV features, when combined with morphology, often yielded the most accurate predictions, with performance reaching a maximum of r = 0.33. These results demonstrate the potential of low-cost, image-based phenomic data to inform early-stage selection and support the development of sustainable perennial crops. Plain language summaryMany of the worlds most important crops (e.g., wheat, maize, soy) live for less than one year, leaving soil bare for many months and subject to erosion. Longer-lived (perennial) alternatives to these crops have the potential to support a more sustainable agricultural system by improving soil health and reducing the need for chemical inputs. However, perennial herbaceous plants were not domesticated by early farmers, and now lag behind annual crops in development. They take longer to breed and often lack the genetic resources needed for modern breeding approaches. In this study, we tested whether simple seed images derived from color scanners could be used to predict key traits like germination proportion and timing in emerging perennial crops. We found that seed color and shape varied by maternal family and that color, especially when combined with seed size and shape, could help predict germination performance. These predictions were significantly better than chance. This work shows that low-cost, image-based seed data can support early-stage selection in breeding programs, especially for undeveloped perennial crops, helping save time, reduce cost, and accelerate progress toward more sustainable agriculture.

plant biology↗

bioRxiv2019

Heating quinoa shoots results in yield loss by inhibiting fruit production and delaying maturity

Increasing global temperatures and a growing world population create the need to develop crop varieties that yield more in warmer climates. There is growing interest in expanding quinoa cultivation, because of quinoas ability to produce nutritious grain in poor soils, with little water and at high salinity. However, the main limitation to expanding quinoa cultivation is quinoas susceptibility to temperatures above ~32{degrees}C. This study investigates the phenotypes, genes, and mechanisms that may affect quinoa seed yield at high temperatures. By using a differential heating system where only roots or only shoots were heated, quinoa yield losses were attributed to shoot heating. Plants with heated shoots lost 60% to 85% yield as compared to control. Yield losses were due to lower fruit production, which lowered the number of seeds produced per plant. Further, plants with heated shoots had delayed maturity and more non-reproductive shoot biomass, while plants with both heated roots and heated shoots produced more yield from panicles that escaped heat than control. This suggests that quinoa uses a type of avoidance strategy to survive heat. Gene expression analysis identified transcription factors differentially expressed in plants with heated shoots and low yield that had been previously associated with flower development and flower opening. Interestingly, in plants with heated shoots, flowers stayed closed during the day while control flowers were open. Although a closed flower may protect floral structures, this could also cause yield losses by limiting pollen dispersal, which is necessary to produce fruit in quinoas mostly female flowers.\n\nSignificance StatementThis study provides evidence that heating quinoa during flowering results in seed yield loss by lowering fruit production. Plants with low yield after heat treatment also matured more slowly, suggesting that quinoa may use a type of avoidance strategy to survive heat stress conditions. Genes differentially expressed under heat include genes involved in flower development and flower opening.

plant biology↗