Assessing plant performance in the Enviratron

Background: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. Results: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. Conclusions: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ—in that the rover takes sensors to the plants rather than moving plants to the sensors

Potential volcanic impacts on future climate variability

Volcanic activity plays a strong role in modulating climate variability (ref. 1). Most model projections of the twenty-first century, however, under-sample future volcanic effects by not representing the range of plausible eruption scenarios (ref. 2,3,4). Here, we explore how sixty possible volcanic futures, consistent with ice-core records (ref. 5), impact climate variability projections of the Norwegian Earth System Model (NorESM) (ref. 6) under RCP4.5 (ref. 7). The inclusion of volcanic forcing enhances climate variability on annual-to-decadal timescales. Although decades with negative global temperature trends become ∼50% more commonplace with volcanic activity, these are unlikely to be able to mitigate long-term anthropogenic warming. Volcanic activity also impacts probabilistic projections of global radiation, sea level, ocean circulation, and sea-ice variability, the local-scale effects of which are detectable when quantifying the time of emergence (ref. 8). These results highlight the importance and feasibility of representing volcanic uncertainty in future climate assessments

A robotic proximal sensing platform for in-field high-throughput crop phenotyping

A rapidly increasing world population and changing climate means plant scientists will need to be able to efficiently develop crop varieties to feed the world. Although the technology for sequencing the genomes of plants has advanced, the technology for characterizing the physical traits of plants has remained relatively static. This gap in technology has become known as the “Phenotyping Bottleneck.” To close this gap, researchers are working to develop robotic systems that can efficiently recognize various physical traits of plants. The goal of this research was to investigate how the design requirements for a vehicle that interacts with a biological system are translated into a working mechanical system. The design requirements include the ability to traverse and image crops in 30-inch wide space between crop rows. This thesis reports the concepts, design decisions, and manufacturing process around the construction of such as a phenotyping robot namely Phenobot 3.0, which stands for the 3rd generation of our phenotyping robot series. Phenobot 3.0 is optimized for phenotyping maize plants in the field but can be adapted for phenotyping other crops. Specifically, Phenobot 3.0 is designed to be narrow to fit between the rows but also tall for sensor placement so that it can gather data from the emergence to the full height of maize plants. To achieve the needed stability of the sensors (LiDAR, stereo cameras), the robot employs a self-leveling mast to cope with the uneven terrain while ensuring proper sensor to plant placement. Unlike many other field-based phenotyping robots, Phenobot 3.0 employs a 4-wheel-drive articulated drivetrain that has differentials on each pair of wheels to ensure maximum steering efficiency and prolonged operational time in the field. Phenobot 3.0 will be a member of PhenoNet, a network of five robots for maize plant phenotyping under different growing environments, a project funded by the National Science Foundation. The scale of this project implies that each design requirement must be carefully evaluated so that the manufacturing process can be easily scaled up to produce multiple units. The results from the preliminary tests of the Phenobot 3.0 prototype have demonstrated satisfactory functionalities and expected performance metrics.</p

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.This is the published version of the following article: Xiang, Lirong, Trevor M. Nolan, Yin Bao, Mitch Elmore, Taylor Tuel, Jingyao Gai, Dylan Shah et al. "Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses." The Plant Journal 107, no. 6 (2021): 1837-1853. DOI: 10.1111/tpj.15401. Copyright 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Posted with permission

Assessing plant performance in the Enviratron

Background: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. Results: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. Conclusions: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ—in that the rover takes sensors to the plants rather than moving plants to the sensors.This article is published as Bao, Yin, Scott Zarecor, Dylan Shah, Taylor Tuel, Darwin A. Campbell, Antony VE Chapman, David Imberti, Daniel Kiekhaefer, Henry Imberti, Thomas Lübberstedt, Yanhai Yin, Dan Nettleton, Carolyn J. Lawrence‑Dill, Steven A. Whitham, Lie Tang, and Stephen H. Howell. "Assessing plant performance in the Enviratron." Plant Methods 15, no. 1 (2019): 117. DOI: 10.1186/s13007-019-0504-y. Posted with permission.</p

Enhanced stability of natural anthocyanin incorporated in Fe-containing mesoporous silica

NOTICE: this is the author’s version of a work that was accepted for publication in Microporous and Mesoporous Materials. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.Changes may have been made to this work since it was submitted for publication.A definitive version was subsequently published inMicroporous and Mesoporous Materials, VOL203, February 2015. doi:10.1016/j.micromeso.2014.10.042autho