Assessing plant performance in the Enviratron

Opportunities and limits of controlled-environment plant phenotyping for climate response traits

Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.

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.

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.

Daily temperature cycles promote alternative splicing of RNAs encoding SR45a, a splicing regulator in maize

Elevated temperatures enhance alternative RNA splicing in maize (Zea mays) with the potential to expand the repertoire of plant responses to heat stress. Alternative RNA splicing generates multiple RNA isoforms for many maize genes, and here we observed changes in the pattern of RNA isoforms with temperature changes. Increases in maximum daily temperature elevated the frequency of the major modes of alternative splices (AS), in particular retained introns and skipped exons. The genes most frequently targeted by increased AS with temperature encode factors involved in RNA processing and plant development. Genes encoding regulators of alternative RNA splicing were themselves among the principal AS targets in maize. Under controlled environmental conditions, daily changes in temperature comparable to field conditions altered the abundance of different RNA isoforms, including the RNAs encoding the splicing regulator SR45a, a member of the SR45 gene family. We established an "in protoplast" RNA splicing assay to show that during the afternoon on simulated hot summer days, SR45a RNA isoforms were produced with the potential to encode proteins efficient in splicing model substrates. With the RNA splicing assay, we also defined the exonic splicing enhancers that the splicing-efficient SR45a forms utilize to aid in the splicing of model substrates. Hence, with rising temperatures on hot summer days, SR45a RNA isoforms in maize are produced with the capability to encode proteins with greater RNA splicing potential.

The Transcription Factor bZIP60 Links the Unfolded Protein Response to the Heat Stress Response in Maize

The unfolded protein response (UPR) and the heat shock response (HSR) are two evolutionarily conserved systems that protect plants from heat stress. The UPR and HSR occur in different cellular compartments and both responses are elicited by misfolded proteins that accumulate under adverse environmental conditions such as heat stress. While the UPR and HSR appear to operate independently, we have found a link between them in maize (Zea mays) involving the production of the BASIC LEUCINE ZIPPER60 (bZIP60) transcription factor, a pivotal response of the UPR to heat stress. Surprisingly, a mutant (bzip60-2) knocking down bZIP60 expression blunted the HSR at elevated temperatures and prevented the normal upregulation of a group of heat shock protein genes in response to elevated temperature. The expression of a key HEAT SHOCK FACTOR TRANSCRIPTION FACTOR13 (HSFTF13, a HEAT SHOCK FACTOR A6B [HSFA6B] family member) was compromised in bzip60-2, and the HSFTF13 promoter was shown to be a target of bZIP60 in maize protoplasts. In addition, the upregulation by heat of genes involved in chlorophyll catabolism and chloroplast protein turnover were subdued in bzip60-2, and these genes were also found to be targets of bZIP60. Thus, the UPR, an endoplasmic-reticulum-associated response, quite unexpectedly contributes to the nuclear/cytoplasmic HSR in maize.

Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing

To help address the global growing demand for food and fiber, selective breeding programs aim to cultivate crops with higher yields and more resistance to stress. Measuring phenotypic traits needed for breeding programs is usually done manually and is labor-intensive, subjective, and lacks adequate temporal resolution. This paper presents a Multipurpose Autonomous Robot of Intelligent Agriculture (MARIA), an open source differential drive robot that is able to navigate autonomously indoors and outdoors while conducting plant morphological trait phenotyping and soil sensing. For the design of the rover, a drive system was developed using the Robot Operating System (ROS), which allows for autonomous navigation using Global Navigation Satellite Systems (GNSS). For phenotyping, the robot was fitted with an actuated LiDAR unit and a depth camera that can estimate morphological traits of plants such as volume and height. A three degree-of-freedom manipulator mounted on the mobile platform was designed using Dynamixel servos that can perform soil sensing and sampling using off-the-shelf and 3D printed components. MARIA was able to navigate both indoors and outdoors with an RMSE of 0.0156 m and 0.2692 m, respectively. Additionally, the onboard actuated LiDAR sensor was able to estimate plant volume and height with an average error of 1.76% and 3.2%, respectively. The manipulator performance tests on soil sensing was also satisfactory. This paper presents a design for a differential drive mobile robot built from off-the-shelf components that makes it replicable and available for implementation by other researchers. The validation of this system suggests that it may be a valuable solution to address the phenotyping bottleneck by providing a system capable of navigating through crop rows or a greenhouse while conducting phenotyping and soil measurements.