Vertical growth of plants is a dynamic process that is influenced by genetic and environmental factors and has a pronounced effect on overall plant architecture and biomass composition. We have performed six controlled growth trials of an interspecific Setaria italica x Setaria viridis recombinant inbred line population to assess how the genetic architecture of plant height is influenced by developmental queues, water availability and planting density. The non-destructive nature of plant height measurements has enabled us to monitor height throughout the plant life cycle in both field and controlled environments. We find that plant height is reduced under water limitation and high density planting and affected by growth environment (field vs. growth chamber). The results support a model where plant height is a heritable, polygenic trait and that the major genetic loci that influence plant height function independent of growth environment. The identity and contribution of loci that influence height changes dynamically throughout development and the reduction of growth observed in water limited environments is a consequence of delayed progression through the genetic program which establishes plant height in Setaria. In this population, alleles inherited from the weedy S. viridis parent act to increase plant height early, whereas a larger number of small effect alleles inherited from the domesticated S. italica parent collectively act to increase plant height later in development. Growth is a dynamic process that responds to a changing environment. Most of the methods that we have for measuring are static and collecting information throughout an organisms lifecycle is labor and cost prohibitive. Advances in imaging and robotics technology have enabled novel approaches to understanding how plants adapt to the environment. Using the model grass Setaria and new methods for measuring parameters from images, we investigate the genetic architecture of plant height in response to water availability and planting density. Height is one of the most influential components of plant architecture, determining tradeoffs between competition and resource allocation and is an important trait for boosting yields. The non-destructive nature of plant height measurements has enabled us to monitor growth throughout the plant life cycle in both field and controlled environments. We identified several loci controlling height in a population derived from a wild strain of Setaria viridis and its domesticated relative Setaria italica, as well as the developmental time in which these loci act. In this population, alleles inherited from the wild parent act to increase plant height early, whereas a larger number of small effect alleles inherited from the domesticated parent collectively act to increase plant height later in development.

Plant growth and water use are interrelated processes influenced by genetically controlled morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvement of WUE using traditional methods has proven difficult due to the low throughput and environmental heterogeneity of field settings. To overcome these limitations, this study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica × Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use are correlated strongly in this system; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use, and WUE all were heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions can be utilized to predictably modulate trait values given a specified precipitation regime in the model C₄ genus Setaria.

The basic Helix-Loop-Helix (bHLH) transcription factors SCREAM/ICE1 and SCREAM2 have well-characterized roles in the terminal differentiation of stomatal guard cells in Arabidopsis thaliana. Here we report on the characterization of the functional roles of the remaining members of sub-group IIIB, bHLH093 and bHLH061. The bhlh093/bhlh061 double mutant failed to produce a primary inflorescence shoot and displayed greater phenotypic severity than bhlh093 and bhlh061 single mutants. An ultrastructural investigation revealed structural defects that develop in tissues surrounding the meristem prior to inflorescence emergence. The transition to flowering was restored in bhlh093/bhlh061 with the application of gibberellin to the apex. We also demonstrate that gibberellin application alleviates structural defects that develop in tissues surrounding the meristem and restore meristem activity. Furthermore, the bhlh093/bhlh061 double mutant was affected by delayed flowering, and the severity of the phenotype correlated with photoperiod and light intensity. Our results indicate that bHLH093 and bHLH061 function in the gibberellin-mediated establishment of functional apical meristems during the transition from vegetative to reproductive growth.

A comprehensive survey and subject‐expert interviews conducted among agricultural researchers investigated perceived value and barriers to the adoption of unoccupied aerial systems (UASs) in agricultural research. These systems are often referred to colloquially as drones and are composed of unoccupied/uncrewed/unmanned vehicles and incorporated sensors. This study of UASs involved 154 respondents from 21 countries representing various agricultural sectors. The survey identified three key applications considered most promising for UASs in agriculture: precision agriculture, crop phenotyping/plant breeding, and crop modeling. Over 80% of respondents rated UASs for phenotyping as valuable, with 47.6% considering them very valuable. Among the participants, 41% were already using UAS technology in their research, while 49% expressed interest in future adoption. Current users highly valued UASs for phenotyping, with 63.9% considering them very valuable, compared to 39.4% of potential future users. The study also explored barriers to UAS adoption. The most commonly reported barriers were the “High cost of instruments/devices or software” (46.0%) and the “Lack of knowledge or trained personnel to analyze data” (40.9%). These barriers persisted as top concerns for both current and potential future users. Respondents expressed a desire for detailed step‐by‐step protocols for drone data processing pipelines (34.7%) and in‐person training for personnel (16.5%) as valuable resources for UAS adoption. The research sheds light on the prevailing perceptions and challenges associated with UAS usage in agricultural research, emphasizing the potential of UASs in specific applications and identifying crucial barriers to address for wider adoption in the agricultural sector.

Tuber size, shape, colorimetric characteristics, and defect susceptibility are all factors that influence the acceptance of new potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is substantially limited by our inability to precisely measure these features quantitatively on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we developed a low‐cost, semiautomated workflow to capture data and measure each of these characteristics using machine vision. This workflow was applied to assess the phenotypic variation present within 189 F1 progeny of the A08241 breeding population. Machine vision was applied to estimate linear and volumetric tuber size, assess tuber shape characteristics using aspect ratio and biomass profiles, and quantify tuber skin and flesh color; additionally, a deep learning mode was developed to classify the presence of hollow‐heart defect. Our results provide an example of quantitative measurements acquired using machine vision methods that are reliable, heritable, and capable of being used to understand and select multiple traits simultaneously in structured potato breeding populations.