Name: Robotic Sensing and Stimuli Provision for Guided Plant Growth (Video) | JoVE Journal
Uploaded: 2019-07-01T15:00:00.000+00:00
Duration: 8 min 2 s
Description: Learn to guide plant growth using autonomous robotic nodes with blue light stimuli in Phaseolus vulgaris. This biohybrid method demonstrates controlled phototropism for agricultural applications.

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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.

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</ol>

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

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="Electronic component diagram with photoresistor, IR sensor, RGB LED; labeled parts for study." class="xfigimg" src="/files/ftp_upload/59835/59835fig1.jpg" data-altupdatedat="1788282455000" data-alt="Figure 1"> 
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="Raspberry Pi control diagram with sensors, LED drivers, ADC, and GPIO setup for automation." class="xfigimg" src="/files/ftp_upload/59835/59835fig2.jpg" data-altupdatedat="1788282455000" data-alt="Figure 2"> 
Figure 2. The component diagram of the immobile robot electronics. IRLML2060 LED drivers are interfaced with the robot'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="IR sensor measurements; A: IR data graph over time, B: IR-proximity setup diagram with plant tip." class="xfigimg" src="/files/ftp_upload/59835/59835fig3.jpg" data-altupdatedat="1788282455000" data-alt="Figure 3"> 
Figure 3. Shortly after '03.04.16,' 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="Plant growth direction diagram; Y-maze setup; phototropism experiment; light response analysis." class="xfigimg" src="/files/ftp_upload/59835/59835fig4.jpg" data-altupdatedat="1788282455000" data-alt="Figure 4"> 
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="Plant growth under varied LED light setups; botanical experiment, photosynthetic response analysis." class="xfigimg" src="/files/ftp_upload/59835/59835fig5.jpg" data-altupdatedat="1788282455000" data-alt="Figure 5"> 
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' 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="Static equilibrium diagram with plant growth under LED lights and structural frame analysis." class="xfigimg" src="/files/ftp_upload/59835/59835fig6v2.jpg" data-altupdatedat="1788282455000" data-alt="Figure 6"> 
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' 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>

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

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.

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

Source: Nathoo, N., et al. A Hydroponic Co-cultivation System for Simultaneous and Systematic Analysis of Plant/Microbe Molecular Interactions and Signaling. J. Vis. Exp. (2017).This video demonstrates a hydroponic co-cultivation system for studying plant–bacteria interactions under sterile conditions. Plant seedlings are transferred into a sterile, acidic growth medium, incubated, and inspected for contamination before inoculation with Agrobacterium. The activated bacteria attach to plant...

A Co-Cultivation System to Study the Molecular Responses of Plant Hosts and Interacting Bacteria

This protocol shows a plant sample preparation method for light-sheet microscopy. The setup is characterized by mounting the plant vertically on the surface of a gel and letting it grow in controlled bright conditions. This allows long-term observation of plant organ development in standardized conditions.

Light Sheet Fluorescence Microscopy of Plant Roots Growing on the Surface of a Gel

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.

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

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.

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

Gibberellin Perception Sensor 1 (GPS1) is the first Förster 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.

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

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.

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

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–environment interactions.

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

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.

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

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 for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

This manuscript describes a non-destructive, high-throughput approach for autonomous field measurements of photosystem II quantum efficiency, spectral reflectance, and plant architecture, enabling large-scale canopy photosynthesis phenotyping in agronomic and breeding field trials.

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

Learn to guide plant growth using autonomous robotic nodes with blue light stimuli in Phaseolus vulgaris. This biohybrid method demonstrates controlled phototropism for agricultural applications.

Robotic Sensing and Stimuli Provision for Guided Plant Growth

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