Name: robotic sensing and stimuli provision for guided plant growth
Uploaded: 2019-07-01T15:00:00.000+00:00
Duration: 8 min 2 s
Description: Watch this Scientific Journal Video about Robotic Sensing and Stimuli Provision for Guided Plant Growth at JoVE.com

This study was supported by flora robotica project that received funding from the European Union&#39;s Horizon 2020 research and innovation programme under the FET grant agreement, no. 640959. The authors thank Anastasios Getsopulos and Ewald Neufeld for their contribution in hardware assembly, and Tanja Katharina Kaiser for her contribution in monitoring plant experiments.

<ol>
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The authors declare that they have no competing financial interests.

The presented methodology shows initial steps toward automating the stimuli-driven steering of plant growth, to generate specific patterns. This requires continuous maintenance of plant health while combining into a single experiment setup the distinct realms of biochemical growth responses and engineered mechatronic functions-sensing, communication, and controlled generation of stimuli. As our focus here is on climbing plants, mechanical support is also integral. A limitation of the current setup is its scale, but we believe our methodology easily scales. The mechanical scaffold can be extended for larger setups and therefore longer periods of growth, which also allows expanded configurations and patterns. Here the setup is limited to two dimensions and binary left-right decisions, as growth is limited to a grid of mechanical supports at 45&#176; inclination, and plant decision positions are limited to that grid&#39;s bifurcations. Mechanical extensions may include 3D scaffolds and differing materials, to allow for complex shapes9,19. The methodology can be considered a system to automatically grow patterns defined by a user. By extending the possible complexity of mechanical configurations, users should face few restrictions on their desired patterns. For such an application, a user software tool should confirm that the pattern is producible, and the mechatronics should then self-organize the production of the pattern by generating appropriate stimuli to steer the plants. The software should also be extended to include recovery plans and policies determining how to continue with the growth if the original planned pattern has partially failed-for instance if the first activated robot has never detected a plant but the dormant ones have seen that the position of the growing tips are beyond the activated robot.
In the presented methodology, an example plant species meeting the protocol selection criteria is the climbing common bean, P. vulgaris. This is the species used in the representative results. As P. vulgaris has strong positive phototropism to UV-A and blue light, the phototropins (light-receptor proteins) in the plant will absorb photons corresponding to wavelengths 340-500 nm. When the receptors are triggered, first swelling will occur in the stem by the preferential relocation of water to the stem tissues opposing the triggered receptors, causing a reversible directional response. Then, within the stem, auxin (plant patterning hormone) is directed to the same tissue location, perpetuating the directional response and fixing stem tissues as they stiffen. This behavior can be used for shaping the plants in these controlled indoor conditions, as the plants are exposed only to isolated blue light and isolated red light, with incident far-red light from IR-proximity sensors at low enough levels that it does not interfere with behaviors such as the shade-avoidance response20,21. The phototropism reaction in the plant responds in the setup to light from blue diodes with peak emission &#411;max = 465 nm, and photosynthesis22,23 in the plant is supported by red diodes with peak emission &#411;max = 650 nm. P. vulgaris growing up to several meters in height is suitable in the overall setup, as the roughly 3 L of commercial gardening soil needed per pot fits the setup scale.
Although the current setup focuses on light as an attraction stimulus, additional stimuli may be relevant for other experiment types. If the desired pattern requires a separation between different groups of plants (e.g., the desired pattern needs two groups of plants to choose opposite sides), then it may not be feasible using only one type of stimulus. For such complex growth patterns independent of scaffold shape, the different groups of plants can potentially be grown in different time periods such that their respective attraction stimuli do not interfere, which would also allow the integration of branching events. However, this may not always be a suitable solution, and the standard attractive light stimulus could then be augmented by repelling influences such as shading, or by other stimuli like far-red light or vibration motors9,14.
The presented method and the experiment design are only an initial first step towards a sophisticated methodology to automatically influence directional growth of plants. The experiment setup is basic by determining only a sequence of binary decisions in the plants and we focus on one, easy to manage stimulus. Additional studies would be required to prove the method&#39;s statistical significance, to add more stimuli, and to control other processes such as branching. With sufficient development to guarantee the long-term reliability of the robots, the presented methodology could allow for automation of plant experiments over long time periods, reducing the overhead associated with the study of plant development stages beyond that of shoots. Similar methods can allow for future investigations into the underexplored dynamics between biological organisms and autonomous robots, when the two act as tightly coupled self-organizing bio-hybrid systems.

Congruent with the increasing prevalence of automation in manufacturing and production, robots are being utilized to sow, treat, and harvest plants1,2,3,4,5. We use robot technology to automate plant experiments in a non-invasive manner, with the purpose of steering growth via directional responses to stimuli. Traditional gardening practices have included the manual shaping of trees and bushes by mechanical restraint and cutting. We present a methodology that can for instance be applied to this shaping task, by steering growth patterns with stimuli. Our presented methodology is also a step towards automated plant experiments, here with a specific focus on providing light stimuli. Once the technology has become robust and reliable, this approach has potential to reduce costs in plant experiments and to allow for new automated experiments that would otherwise be infeasible due to overhead in time and manual labor. The robotic elements are freely programmable and act autonomously as they are equipped with sensors, actuators for stimuli provision, and microprocessors. While we focus here on proximity sensing (i.e., measuring distances at close-range) and light stimuli, many other options are feasible. For example, sensors can be used to analyze plant color, to monitor biochemical activity6, or for phytosensing7 approaches to monitor for instance environmental conditions through plant electrophysiology8. Similarly, actuator options might provide other types of stimuli9, through vibration motors, spraying devices, heaters, fans, shading devices, or manipulators for directed physical contact. Additional actuation strategies could be implemented to provide slow mobility to the robots (i.e., &#39;slow bots&#39;10), such that they could gradually change the position and direction from which they provide stimuli. Furthermore, as the robots are equipped with single-board computers, they could run more sophisticated processes such as visioning for plant phenotyping11 or artificial neural network controllers for stimuli actuation12. As the plant science research focus is often on early growth (i.e., in shoots)13, the whole domain of using autonomous robot systems to influence plants over longer periods seems underexplored and may offer many future opportunities. Going even one step further, the robotic elements can be seen as objects of research themselves, allowing the study of the complex dynamics of bio-hybrid systems formed by robots and plants closely interacting. The robots selectively impose stimuli on the plants, the plants react according to their adaptive behavior and change their growth pattern, which is subsequently detected by the robots via their sensors. Our approach closes the behavioral feedback loop between the plants and the robots and creates a homeostatic control loop.
In our experiments to test the function of the robot system, we exclusively use the climbing common bean, Phaseolus vulgaris. In this setup, we use climbing plants, with mechanical supports in a gridded scaffold of overall height 180 cm, such that the plants are influenced by thigmotropism and have a limited set of growth directions to choose from. Given that we want to shape the whole plant over a period of weeks, we use blue light stimuli to influence the plant&#39;s phototropism macroscopically, over different growth periods including young shoots and later stem stiffening. We conduct the experiments in fully controlled ambient light conditions where other than the blue light stimuli we provide exclusively red light, with peak emission at wavelength 650 nm. When they reach a bifurcation in the mechanical support grid, they make a binary decision whether to grow left or right. The robots are positioned at these mechanical bifurcations, separated by distances of 40 cm. They autonomously activate and deactivate their blue light emittance, with peak emission at wavelength 465 nm, according to a predefined map of the desired growth pattern (in this case, a zigzag pattern). In this way, the plants are guided from bifurcation to bifurcation in a defined sequence. Only one robot is activated at a given time&#8212;during which it emits blue light while autonomously monitoring plant growth on the mechanical support beneath it. Once it detects a growing tip using its infrared proximity sensors, it stops emitting blue light and communicates to its neighboring robots via radio. The robot that determines itself to be the next target in the sequence then subsequently activates, attracting plant growth toward a new mechanical bifurcation.
As our approach incorporates both engineered and natural mechanisms, our experiments include several methods that operate simultaneously and interdependently. The protocol here is first organized according to the type of method, each of which must be integrated into a unified experiment setup. These types are plant species selection; robot design including hardware and mechanics; robot software for communication and control; and the monitoring and maintenance of plant health. The protocol then proceeds with the experiment design, followed by data collection and recording. For full details of results obtained so far, see Wahby et al.14. Representative results cover three types of experiments&#8212;control experiments where all robots do not provide stimuli (i.e., are dormant); single-decision experiments where the plant makes a binary choice between one stimuli-providing robot and one that is dormant; and multiple-decision experiments where the plant navigates a sequence of binary choices to grow a predefined pattern.

<table><tbody><tr><td>3D printed case</td><td>Shapeways, Inc</td><td>n/a</td><td>Customized product, https://www.shapeways.com/</td></tr><tr><td>3D printed joints</td><td>n/a</td><td>n/a</td><td>Produced by authors</td></tr><tr><td>Adafruit BME280 I2C or SPI Temperature Humidity Pressure Sensor</td><td>Adafruit</td><td>2652</td><td></td></tr><tr><td>Arduino Uno Rev 3</td><td>Arduino</td><td>A000066</td><td></td></tr><tr><td>CdS photoconductive cells</td><td>Lida Optical &amp;amp; Electronic Co., Ltd</td><td>GL5528</td><td></td></tr><tr><td>Cybertronica PCB</td><td>Cybertronica Research</td><td>n/a</td><td>Customized product, http://www.cybertronica.de.com/download/D2_node_module_v01_appNote16.pdf</td></tr><tr><td>DC Brushless Blower Fan</td><td>Sunonwealth Electric Machine Industry Co., Ltd.</td><td>UB5U3-700</td><td></td></tr><tr><td>Digital temperature sensor</td><td>Maxim Integrated</td><td>DS18B20</td><td></td></tr><tr><td>High Power (800 mA) EPILED - Far Red / Infra Red (740-745 nm)</td><td>Future Eden Ltd.</td><td>n/a</td><td></td></tr><tr><td>I2C Soil Moisture Sensor</td><td>Catnip Electronics</td><td>v2.7.5</td><td></td></tr><tr><td>IR-proximity sensors (4-30 cm)</td><td>Sharp Electronics</td><td>GP2Y0A41SK0</td><td></td></tr><tr><td>LED flashlight (50 W)</td><td>Inter-Union Technohandel GmbH</td><td>103J50</td><td></td></tr><tr><td>LED Red Blue Hanging Light for Indoor Plant (45 W)</td><td>Erligpowht</td><td>B00S2DPYQM</td><td></td></tr><tr><td>Low-voltage submersible pump 600 l/h (6 m rise)</td><td>Peter Barwig Wasserversorgung</td><td>444</td><td></td></tr><tr><td>Medium density fibreboard</td><td>n/a</td><td>n/a</td><td>For stand</td></tr><tr><td>Micro-Spectrometer (Hamamatsu) on an Arduino-compatible breakout board</td><td>Pure Engineering LLC</td><td>C12666MA</td><td></td></tr><tr><td>Pixie - 3W Chainable Smart LED Pixel</td><td>Adafruit</td><td>2741</td><td></td></tr><tr><td>Pots (3.5 l holding capacity, 15.5 cm in height)</td><td>n/a</td><td>n/a</td><td></td></tr><tr><td>Power supplies (5 V, 10 A)</td><td>Adafruit</td><td>658</td><td></td></tr><tr><td>Raspberry Pi 3 Model B</td><td>Raspberry Pi Foundation</td><td>3B</td><td></td></tr><tr><td>Raspberry Pi Camera Module V2</td><td>Raspberry Pi Foundation</td><td>V2</td><td></td></tr><tr><td>Raspberry Pi Zero</td><td>Raspberry Pi Foundation</td><td>Zero</td><td></td></tr><tr><td>RGB Color Sensor with IR filter and White LED - TCS34725</td><td>Adafruit</td><td>1334</td><td></td></tr><tr><td>Sowing and herb soil</td><td>Gardol</td><td>n/a</td><td></td></tr><tr><td>String bean</td><td>SPERLI GmbH</td><td>402308</td><td></td></tr><tr><td>Transparent acrylic 5 mm sheet</td><td>n/a</td><td>n/a</td><td>For supplemental structural support</td></tr><tr><td>Wooden rods (birch wood), painted black, 5 mm diameter</td><td>n/a</td><td>n/a</td><td>For plants to climb</td></tr></tbody></table>

robotic sensing and stimuli provision for guided plant growth

Robot systems are actively researched for manipulation of natural plants, typically restricted to agricultural automation activities such as harvest, irrigation, and mechanical weed control. Extending this research, we introduce here a novel methodology to manipulate the directional growth of plants via their natural mechanisms for signaling and hormone distribution. An effective methodology of robotic stimuli provision can open up possibilities for new experimentation with later developmental phases in plants, or for new biotechnology applications such as shaping plants for green walls. Interaction with plants presents several robotic challenges, including short-range sensing of small and variable plant organs, and the controlled actuation of plant responses that are impacted by the environment in addition to the provided stimuli. In order to steer plant growth, we develop a group of immobile robots with sensors to detect the proximity of growing tips, and with diodes to provide light stimuli that actuate phototropism. The robots are tested with the climbing common bean, Phaseolus vulgaris, in experiments having durations up to five weeks in a controlled environment. With robots sequentially emitting blue light-peak emission at wavelength 465 nm-plant growth is successfully steered through successive binary decisions along mechanical supports to reach target positions. Growth patterns are tested in a setup up to 180 cm in height, with plant stems grown up to roughly 250 cm in cumulative length over a period of approximately seven weeks. The robots coordinate themselves and operate fully autonomously. They detect approaching plant tips by infrared proximity sensors and communicate via radio to switch between blue light stimuli and dormant status, as required. Overall, the obtained results support the effectiveness of combining robot and plant experiment methodologies, for the study of potentially complex interactions between natural and engineered autonomous systems.

Control: Plant Behavior without Robotic Stimuli. 
Due to the lack of blue light (i.e., all robots are dormant), positive phototropism is not triggered in the plant. Therefore, the plants show unbiased upwards growth as they follow gravitropism. They also display typical circumnutation (i.e., winding), see Figure 4A. As expected, the plants fail to find the mechanical support leading to the dormant robots. The plants collapse when they can no longer support their own weight. We stop the experiments when at least two plants collapse, see Figure 4B,C.
Single or Multiple Decisions: Plant Behavior with Robotic Stimuli 
In four single-decision experiments, two runs have leftward steering (i.e., the robot left of the bifurcation is activated to stimulus), and two runs have rightward steering. The stimulus robots successfully steer the plants towards the correct support, see Figure 5. The nearest plant with stem angle most similar to that of the correct support attaches first. In each experiment, at least one plant attaches to the support and climbs it until it reaches the stimulus robot and thereby ends the experiment. In one experiment, a second plant attaches to the correct support. The remaining plants might attach as well in longer experiment durations. None of the plants attaches to the incorrect support. Each experiment runs continuously for 13 days on average.
In two multiple-decision experiments, the plants grow into a predefined zigzag pattern, see Figure 6A. Each experiment runs for approximately seven weeks. As an experiment starts, a robot sets its status to stimulus (see 3.6.3) and steers the plants towards the correct support according to the stipulated pattern. A plant attaches and climbs it, arriving at the activated stimulus robot therefore completing the first decision. According to 3.7.3, the current stimulus robot then becomes dormant and notifies its adjacent neighbors. The dormant neighbor that is next on the zigzag pattern switches itself to stimulus (see 3.7.6). If a plant is detected by a dormant robot, that robot does not react (see 3.7.2). The plants continue and complete the remaining three decisions successfully. The predefined zigzag pattern is therefore fully grown, see Figure 6B.
All experiment data, as well as videos, are available online24.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59835/59835fig1.jpg" /> 
Figure 1. The immobile robot and its primary components. Figure reprinted from author publication Wahby et al.14, used with Creative Commons license CC-BY 4.0 (see supplemental files), with modifications as permitted by license. <a href="https://www.jove.com/files/ftp_upload/59835/59835fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59835/59835fig2.jpg" /> 
Figure 2. The component diagram of the immobile robot electronics. IRLML2060 LED drivers are interfaced with the robot&#39;s single-board computer (e.g. Raspberry Pi) via PWM to control the brightness of the LEDs. An LP5907 switch is interfaced with the single-board computer via general-purpose input/output (GPIO) header pin, to control the fan. An MCP3008 analog-to-digital converter (ADC) is interfaced with the single-board computer via serial peripheral interface (SPI) to read the analog IR and light-dependent resistor (LDR) sensor data. <a href="https://www.jove.com/files/ftp_upload/59835/59835fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59835/59835fig3.jpg" /> 
Figure 3. Shortly after &#39;03.04.16,&#39; a plant tip climbs a support and arrives in the field of view of the robot. (A) Sample IR-proximity sensor scaled voltage readings (vertical axis) during an experiment. Higher values indicate plant tip detection. (B) The IR-proximity sensor is placed and oriented according to the support attachment, to ensure effective plant tip detection. Figure reprinted from author publication Wahby et al.14, used with Creative Commons license CC-BY 4.0 (see supplemental files), with modifications as permitted by license. <a href="https://www.jove.com/files/ftp_upload/59835/59835fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59835/59835fig4.jpg" /> 
Figure 4. Control experiments result frames showing that all four plants did not attach to any support in the absence of blue light. (A) After five days, all plants growing upwards in one of the control experiments (see (C) for later growth condition). (B) After 15 days, three plants collapsed, and one still growing upwards in the first control experiment. (C) After seven days, two plants collapsed, and two still growing upwards in the second control experiment (see (A) for previous growth condition). Figure reprinted from author publication Wahby et al.14, used with Creative Commons license CC-BY 4.0 (see supplemental files), with modifications as permitted by license. <a href="https://www.jove.com/files/ftp_upload/59835/59835fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59835/59835fig5.jpg" /> 
Figure 5. Single-decision experiments result frames showing the ability of a stimulus robot to steer the plants through a binary decision, to climb the correct support. In all four experiments, one robot is set to stimulus and the other to dormant-at two opposite sides of a junction. The frames show the plants&#39; location right before the stimulus robot detects them. In each experiment at least one plant attaches to the correct support, and no plant attaches to the incorrect one. Also, the unsupported plants show growth biased towards the stimulus robot. E, F, G, H are closeups of A, B, C, D respectively. Figure reprinted from author publication Wahby et al.14, used with Creative Commons license CC-BY 4.0 (see supplemental files), with modifications as permitted by license. <a href="https://www.jove.com/files/ftp_upload/59835/59835fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59835/59835fig6v2.jpg" /> 
Figure 6. Multiple-decision experiment. (A) The targeted zigzag pattern is highlighted in green on the map. (B) The last frame from the experiment (after 40 days), showing the plants&#39; situation before the last stimulus robot on the pattern detects them. The robots successfully grow the zigzag pattern. Figure reprinted from author publication Wahby et al.14, used with Creative Commons license CC-BY 4.0 (see supplemental files), with modifications as permitted by license. <a href="https://www.jove.com/files/ftp_upload/59835/59835fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Robotic Sensing and Stimuli Provision for Guided Plant Growth at JoVE.com

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Distributed robot nodes provide sequences of blue light stimuli to steer the growth trajectories of climbing plants. By activating natural phototropism, the robots guide the plants through binary left-right decisions, growing them into predefined patterns that by contrast are not possible when the robots are dormant.

Robot systems are actively researched for manipulation of natural plants, typically restricted to agricultural automation activities such as harvest, ...

robotic-sensing-and-stimuli-provision-for-guided-plant-growth

Research

JoVE Journal

Engineering

7.9K Views.  University of Lübeck. Het sturen van de directionele groei van planten met een autonoom systeem is een nieuw en origineel concept dat niet eerder werd gemeld. De sleutel hier voor ons is om uit te vinden wat er eigenlijk haalbaar is. Onze techniek is de eerste geautomatiseerde methode om planten te laten groeien tot gewenste vormen en vormen met behulp van robotknooppunten om planten te detecteren met hun sensoren en om de stimuli dienovereenkomstig te schakelen.Deze methode en de robots die hier worden geb...

Video: Robotic Sensing and Stimuli Provision for Guided Plant Growth

Time-lapse Fluorescence Imaging of Arabidopsis Root Growth with Rapid Manipulation of The Root Environment Using The RootChip

The root functions as the physical anchor of the plant and is the organ responsible for uptake of water and mineral nutrients such as nitrogen, phosphorus, sulfate and trace elements that plants acquire from the soil. If we want to develop sustainable approaches to producing high crop yield, we need to better understand how the root develops, takes up a wide spectrum of nutrients, and interacts with symbiotic and pathogenic organisms. To accomplish these goals, we need to be able to explore roots in microscopic detail over time periods ranging from minutes to days.
We developed the RootChip, a polydimethylsiloxane (PDMS)- based microfluidic device, which allows us to grow and image roots from Arabidopsis seedlings while avoiding any physical stress to roots during preparation for imaging1 (Figure 1). The device contains a bifurcated channel structure featuring micromechanical valves to guide the fluid flow from solution inlets to each of the eight observation chambers2. This perfusion system allows the root microenvironment to be controlled and modified with precision and speed. The volume of the chambers is approximately 400 nl, thus requiring only minimal amounts of test solution.
Here we provide a detailed protocol for studying root biology on the RootChip using imaging-based approaches with real time resolution. Roots can be analyzed over several days using time lapse microscopy. Roots can be perfused with nutrient solutions or inhibitors, and up to eight seedlings can be analyzed in parallel. This system has the potential for a wide range of applications, including analysis of root growth in the presence or absence of chemicals, fluorescence-based analysis of gene expression, and the analysis of biosensors, e.g. FRET nanosensors3.

This article provides a protocol for cultivation of Arabidopsis seedlings in the RootChip, a microfluidic imaging platform that combines automated control of growth conditions with microscopic root monitoring and FRET-based measurement of intracellular metabolite levels.

The root functions as the physical anchor of the plant and is the organ responsible for uptake of water and mineral nutrients such as nitrogen, ...

Bioengineering

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

Environment

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&ndash;Environment Interactions

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant&#8211;environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment&#8217;s effects on the plants&#8217; physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (&#952;) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions

This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant&#8211;environment interactions.

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

Visualizing Cellular Gibberellin Levels Using the nlsGPS1 F&#246;rster Resonance Energy Transfer (FRET) Biosensor

The phytohormone gibberellin (GA) is a small, mobile signaling molecule that plays a key role in seed germination, cellular elongation, and developmental transitions in plants. Gibberellin Perception Sensor 1 (GPS1) is the first F&#246;rster resonance energy transfer (FRET)-based biosensor that allows monitoring of cellular GA levels in vivo. By measuring a fluorescence emission ratio of nuclear localized-GPS1 (nlsGPS1), spatiotemporal mapping of endogenously and exogenously supplied GA gradients in different tissue types is feasible at a cellular scale. This protocol will describe how to image nlsGPS1 emission ratios in three example experiments: steady-state, before-and-after exogenous gibberellin A4 (GA4) treatments, and over a treatment time-course. We also provide methods to analyze nlsGPS1 emission ratios using both Fiji and a commercial three-dimensional (3-D) micrograph visualization and analysis software and explain the limitations and likely pitfalls of using nlsGPS1 to quantify gibberellin levels.

Gibberellin Perception Sensor 1 (GPS1) is the first F&#246;rster resonance energy transfer-based biosensor for measuring the cellular levels of gibberellin phytohormones with a high spatiotemporal resolution. This protocol reports on the method to visualize and quantify cellular gibberellin levels using the genetically encoded nlsGPS1 biosensor in Arabidopsis hypocotyls and root tips.

The phytohormone gibberellin (GA) is a small, mobile signaling molecule that plays a key role in seed germination, cellular elongation, and developmental ...

Developmental Biology

Robotics and Dynamic Image Analysis for Studies of Gene Expression in Plant Tissues

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods presented here utilize the green fluorescent protein (gfp) gene for continual monitoring of gene expression in the same pieces of tissues, over time. The gfp gene was placed under regulatory control of different promoters and introduced into lima bean cotyledonary tissues via particle bombardment. Cotyledons were then placed on a robotic image collection system, which consisted of a fluorescence dissecting microscope with a digital camera and a 2-dimensional robotics platform custom-designed to allow secure attachment of culture dishes. Images were collected from cotyledonary tissues every hour for 100 hours to generate expression profiles for each promoter. Each collected series of 100 images was first subjected to manual image alignment using ImageReady to make certain that GFP-expressing foci were consistently retained within selected fields of analysis. Specific regions of the series measuring 300 x 400 pixels, were then selected for further analysis to provide GFP Intensity measurements using ImageJ software. Batch images were separated into the red, green and blue channels and GFP-expressing areas were identified using the threshold feature of ImageJ. After subtracting the background fluorescence (subtraction of gray values of non-expressing pixels from every pixel) in the respective red and green channels, GFP intensity was calculated by multiplying the mean grayscale value per pixel by the total number of GFP-expressing pixels in each channel, and then adding those values for both the red and green channels. GFP Intensity values were collected for all 100 time points to yield expression profiles. Variations in GFP expression profiles resulted from differences in factors such as promoter strength, presence of a silencing suppressor, or nature of the promoter. In addition to quantification of GFP intensity, the image series were also used to generate time-lapse animations using ImageReady. Time-lapse animations revealed that the clear majority of cells displayed a relatively rapid increase in GFP expression, followed by a slow decline. Some cells occasionally displayed a sudden loss of fluorescence, which may be associated with rapid cell death. Apparent transport of GFP across the membrane and cell wall to adjacent cells was also observed. Time lapse animations provided additional information that could not otherwise be obtained using GFP Intensity profiles or single time point image collections.

We report a method for introduction, tracking and quantitative analysis of GFP expression in plant cells. This method utilizes a custom-designed robotics system for semi-continuous image collection from large numbers of samples, over time. We also demonstrate the use of ImageJ and ImageReady for analysis of image series.

Gene expression in plant tissues is typically studied by destructive extraction of compounds from plant tissues for in vitro analyses. The methods ...

Biology

A Hydroponic Co-cultivation System for Simultaneous and Systematic Analysis of Plant/Microbe Molecular Interactions and Signaling

An experimental design mimicking natural plant-microbe interactions is very important to delineate the complex plant-microbe signaling processes. Arabidopsis thaliana-Agrobacterium tumefaciens provides an excellent model system to study bacterial pathogenesis and plant interactions. Previous studies of plant-Agrobacterium interactions have largely relied on plant cell suspension cultures, the artificial wounding of plants, or the artificial induction of microbial virulence factors or plant defenses by synthetic chemicals. However, these methods are distinct from the natural signaling in planta, where plants and microbes recognize and respond in spatial and temporal manners. This work presents a hydroponic cocultivation system where intact plants are supported by metal mesh screens and cocultivated with Agrobacterium. In this cocultivation system, no synthetic phytohormone or chemical that induces microbial virulence or plant defense is supplemented. The hydroponic cocultivation system closely resembles natural plant-microbe interactions and signaling homeostasis in planta. Plant roots can be separated from the medium containing Agrobacterium, and the signaling and responses of both the plant hosts and the interacting microbes can be investigated simultaneously and systematically. At any given timepoint/interval, plant tissues or bacteria can be harvested separately for various &#34;omics&#34; analyses, demonstrating the power and efficacy of this system. The hydroponic cocultivation system can be easily adapted to study: 1) the reciprocal signaling of diverse plant-microbe systems, 2) signaling between a plant host and multiple microbial species (i.e.&#160;microbial consortia or microbiomes), 3) how nutrients and chemicals are implicated in plant-microbe signaling, and 4) how microbes interact with plant hosts and contribute to plant tolerance to biotic or abiotic stresses.

The described hydroponic cocultivation system supports intact plants with metal mesh screens and cocultivates them with bacteria. Plant tissue, bacteria, and secreted molecules can then be separately harvested for downstream analyses, simultaneously allowing for the molecular responses of both plant hosts and interacting microbes or microbiomes to be investigated.

An experimental design mimicking natural plant-microbe interactions is very important to delineate the complex plant-microbe signaling processes. ...

A Labor-saving and Repeatable Touch-force Signaling Mutant Screen Protocol for the Study of Thigmomorphogenesis of a Model Plant Arabidopsis thaliana

Plants responding to both intracellular and extracellular mechanical stimulations (or force signals) and develop special morphological changes, a called thigmomorphogenesis. In past decades, several signaling components have been identified and reported for being involved in the mechanotransduction (e.g., calcium ion binding proteins and jasmonic acid biosynthesis enzymes). However, the relatively slow pace of research in the study of force signaling or thigmomorphogenesis is largely attributed to two reasons: the requirement for laborious human hand-manipulated touch induction of thigmomorphogenesis and the force strength errors associated with people&#8217;s hand-touch. To enhance the efficiency of external force loading on a plant organism, an automatic touch-force loading machine was built. This robotic arm-driven hair brush touches provide a labor-saving and easily repeatable touch-force simulation, unlimited rounds of touch repetition and adjustable touch strength. This hair touch-force loading machine can be used for both large scale screening of touch-force signaling mutants and the phenomics study of plant thigmomorphogenesis. In addition, touch materials such as human hair, can be replaced with other natural materials like animal hair, silk threads and cotton fibers. The automated moving arms on the machine may be equipped with water sprinkling nozzles and air blowers to mimic the natural forces of rain drops and wind, respectively. By using this automatic hair touch-force loading machine in combination with the hand-performed cotton swab touching, we have investigated the touch response of two force signaling mutants, MAP KINASE KINASE 1 (MKK1) and MKK2 plants. The phenomes of the touch-force loaded wild type plants and two mutants were evaluated statistically. They have exhibited significant differences in touch response.

A Labor-saving and Repeatable Touch-force Signaling Mutant Screen Protocol for the Study of Thigmomorphogenesis of a Model Plant Arabidopsis thaliana

A gentle touch-force loading machine is built from human hair brushes, robotic arms and a controller. The hair brushes are driven by robotic arms installed on the machine and move periodically to apply touch-force on plants. The strength of machine-driven hair touches is comparable to that of manually applied touches.

Plants responding to both intracellular and extracellular mechanical stimulations (or force signals) and develop special morphological changes, a called ...

Wide-Field, Real-Time Imaging of Local and Systemic Wound Signals in Arabidopsis

Plants respond to mechanical stresses such as wounding and herbivory by inducing defense responses both in the damaged and in the distal undamaged parts. Upon wounding of a leaf, an increase in cytosolic calcium ion concentration (Ca2+ signal) occurs at the wound site. This signal is rapidly transmitted to undamaged leaves, where defense responses are activated. Our recent research revealed that glutamate leaking from the wounded cells of the leaf into the apoplast around them serves as a wound signal. This glutamate activates glutamate receptor-like Ca2+ permeable channels, which then leads to long-distance Ca2+ signal propagation throughout the plant. The spatial and temporal characteristics of these events can be captured with real-time imaging of living plants expressing genetically encoded fluorescent biosensors. Here we introduce a plant-wide, real-time imaging method to monitor the dynamics of both the Ca2+ signals and changes in apoplastic glutamate that occur in response to wounding. This approach uses a wide-field fluorescence microscope and transgenic Arabidopsis plants expressing Green Fluorescent Protein (GFP)-based Ca2+ and glutamate biosensors. In addition, we present methodology to easily elicit wound-induced, glutamate-triggered rapid and long-distance Ca2+ signal propagation. This protocol can also be applied to studies on other plant stresses to help investigate how plant systemic signaling might be involved in their signaling and response networks.

Wide-Field, Real-Time Imaging of Local and Systemic Wound Signals in Arabidopsis

Extracellular glutamate-triggered systemic calcium signaling is critical for the induction of plant defense responses to mechanical wounding and herbivore attack in plants. This article describes a method to visualize the spatial and temporal dynamics of both these factors using Arabidopsis thaliana plants expressing calcium- and glutamate-sensitive fluorescent biosensors.

Plants respond to mechanical stresses such as wounding and herbivory by inducing defense responses both in the damaged and in the distal undamaged parts. ...

Potato Resilience Analysis Under Climate-Related Stresses

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to reveal&#160;the dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

We designed an image-based phenotyping protocol to determine the morphological and physiological responses to single and combined heat, drought, and waterlogging treatments. This approach enabled the identification of early, late, and recovery responses at a whole plant level, particularly above-ground parts, and highlighted the necessity of using multiple imaging sensors.

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions ...

Robotic Sensing and Stimuli Provision for Guided Plant Growth

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