Name: a telemetric, gravimetric platform for real-time physiological phenotyping of plant&ndash;environment interactions
Uploaded: 2020-08-05T13:00:00
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Description: Watch this Scientific Journal Video about A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of PlantÃ¢â‚¬â€œEnvironment Interactions at JoVE.com

This work was supported by the ISF-NSFC joint research program (grant No. 2436/18) and was also partially supported by the Israel Ministry of Agriculture and Rural Development (Eugene Kandel Knowledge Centers) as part of the Root of the Matter &#8211; The Root Zone Knowledge Center for Leveraging Modern Agriculture.

<ol>
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R., Stavness, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+Plant+Phenomics:+A+Deep+Learning+Platform+for+Complex+Plant+Phenotyping+Tasks.">Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks.</a> Frontiers in Plant Science. , (2017).</li><li>Danzi, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+High+Throughput+Phenotyping+Help+Food+Security+in+the+Mediterranean+Area.">Can High Throughput Phenotyping Help Food Security in the Mediterranean Area.</a> Frontiers in Plant Science. , (2019).</li><li>Miflin, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Crop+improvement+in+the+21st+century.">Crop improvement in the 21st century.</a> Journal of Experimental Botany. 51, 1-8 (2000).</li><li>Dalal, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+Physiological+Phenotyping+of+Drought-Stressed+Pepper+Plants+Treated+With+"Productivity-Enhancing"+and+"Survivability-Enhancing"+Biostimulants.">Dynamic Physiological Phenotyping of Drought-Stressed Pepper Plants Treated With "Productivity-Enhancing" and "Survivability-Enhancing" Biostimulants.</a> Frontiers in Plant Science. , (2019).</li><li>Moshelion, M., Altman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+challenges+and+future+perspectives+of+plant+and+agricultural+biotechnology.">Current challenges and future perspectives of plant and agricultural biotechnology.</a> Trends in Biotechnology. 33, 337-342 (2015).</li><li>Singh, A., Ganapathysubramanian, B., Singh, A. K., Sarkar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Machine+Learning+for+High-Throughput+Stress+Phenotyping+in+Plants.">Machine Learning for High-Throughput Stress Phenotyping in Plants.</a> Trends in Plant Science. 21, 110-124 (2016).</li><li>Houle, D., Govindaraju, D. R., Omholt, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenomics:+The+next+challenge.">Phenomics: The next challenge.</a> Nature Reviews Genetics. 11, 855-866 (2010).</li><li>Fiorani, F., Schurr, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Future+Scenarios+for+Plant+Phenotyping.">Future Scenarios for Plant Phenotyping.</a> Annual Review of Plant Biology. 64, 267-291 (2013).</li><li>Tardieu, F., Cabrera-Bosquet, L., Pridmore, T., Bennett, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Plant+Phenomics,+From+Sensors+to+Knowledge.">Plant Phenomics, From Sensors to Knowledge.</a> Current Biology. 27, 770-783 (2017).</li><li>Negin, B., Moshelion, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+advantages+of+functional+phenotyping+in+pre-field+screening+for+drought-tolerant+crops.">The advantages of functional phenotyping in pre-field screening for drought-tolerant crops.</a> Functional Plant Biology. , (2017).</li><li>Gebremedhin, A., Badenhorst, P. E., Wang, J., Spangenberg, G. C., Smith, K. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prospects+for+measurement+of+dry+matter+yield+in+forage+breeding+programs+using+sensor+technologies.">Prospects for measurement of dry matter yield in forage breeding programs using sensor technologies.</a> Agronomy. 9, 65 (2019).</li><li>Roitsch, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+sensors+and+data-driven+approaches-A+path+to+next+generation+phenomics.">New sensors and data-driven approaches-A path to next generation phenomics.</a> Plant Science. 282, 2-10 (2019).</li><li>Li, L., Zhang, Q., Huang, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+imaging+techniques+for+plant+phenotyping.">A review of imaging techniques for plant phenotyping.</a> Sensors (Switzerland). 14, 20078-20111 (2014).</li><li>Gosa, S. C., Lupo, Y., Moshelion, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+and+comparative+analysis+of+whole-plant+performance+for+functional+physiological+traits+phenotyping:+New+tools+to+support+pre-breeding+and+plant+stress+physiology+studies.">Quantitative and comparative analysis of whole-plant performance for functional physiological traits phenotyping: New tools to support pre-breeding and plant stress physiology studies.</a> Plant Science. 282, 49-59 (2019).</li><li>Araus, J. L., Cairns, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Field+high-throughput+phenotyping:+the+new+crop+breeding+frontier.">Field high-throughput phenotyping: the new crop breeding frontier.</a> Trends in Plant Science. 19, 52-61 (2014).</li><li>Ito, V. C., Lacerda, L. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Black+rice+(Oryza+sativa+L.):+A+review+of+its+historical+aspects,+chemical+composition,+nutritional+and+functional+properties,+and+applications+and+processing+technologies.">Black rice (Oryza sativa L.): A review of its historical aspects, chemical composition, nutritional and functional properties, and applications and processing technologies.</a> Food Chemistry. 301, 125304 (2019).</li><li>Anjum, S. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=physiological+and+biochemical+responses+of+plants+to+drought+stress.">physiological and biochemical responses of plants to drought stress.</a> African Journal of Agricultural Research. , (2011).</li><li>Halperin, O., Gebremedhin, A., Wallach, R., Moshelion, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+physiological+phenotyping+and+screening+system+for+the+characterization+of+plant-environment+interactions.">High-throughput physiological phenotyping and screening system for the characterization of plant-environment interactions.</a> The Plant Journal. 89, 839-850 (2017).</li><li>Yaaran, A., Negin, B., Moshelion, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+guard-cell+ABA+in+determining+steady-state+stomatal+aperture+and+prompt+vapor-pressure-deficit+response.">Role of guard-cell ABA in determining steady-state stomatal aperture and prompt vapor-pressure-deficit response.</a> Plant Science. 281, 31-40 (2019).</li><li>Dalal, A., Attia, Z., Moshelion, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=To+produce+or+to+survive:+how+plastic+is+your+crop+stress+physiology.">To produce or to survive: how plastic is your crop stress physiology.</a> Frontiers in Plant Science. 8, 2067 (2017).</li><li>Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., Cairns, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translating+High-Throughput+Phenotyping+into+Genetic+Gain.">Translating High-Throughput Phenotyping into Genetic Gain.</a> Trends in Plant Science. 23, 451-466 (2018).</li><li>Ghanem, M. E., Marrou, H., Sinclair, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physiological+phenotyping+of+plants+for+crop+improvement.">Physiological phenotyping of plants for crop improvement.</a> Trends in Plant Science. 20, 139-144 (2015).</li><li>Sinclair, T. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pot+binding+as+a+variable+confounding+plant+phenotype:+theoretical+derivation+and+experimental+observations.">Pot binding as a variable confounding plant phenotype: theoretical derivation and experimental observations.</a> Planta. 245, 729-735 (2017).</li></ol>

The authors have nothing to disclose.

The genotype&#8211;phenotype knowledge gap reflects the complexity of genotype x environment interactions (reviewed by18,24). It might be possible to bridge this gap through the use of high-resolution, HTP-telemetric diagnostic and phenotypic screening platforms that can be used to study whole-plant physiological performance and water-relation kinetics8,9. The complexity of genotype x environment interactions makes phenotyping a challenge, particularly in light of how rapidly plants respond to their changing environments. Although various phenotyping systems are currently available, most of those systems are based on remote sensing and advanced imaging techniques. Although those systems provide simultaneous measurements, to a certain extent, their measurements are limited to morphological and indirect physiological traits25. Physiological traits are very important in the context of responsiveness or sensitivity to environmental conditions26. Therefore, direct measurements taken continuously and simultaneously at a very high resolution (e.g., 3 min intervals) can provide a very accurate description of a plant&#8217;s physiological behavior. Despite those substantial advantages of the gravimetric system, the fact that this system has some potential disadvantages must also be taken into account. The main disadvantages result from the need to work with pots and in greenhouse conditions, which can present major challenges for treatment-regulation (particularly the regulation of drought treatments) and experimental-repeatability.
In order to address these issues, one should standardize the applied stresses, create a truly randomized experimental structure, minimize pot effects and compare multiple dynamic behaviors of plants under changing environmental conditions within a short period of time. The HTP-telemetric functional phenotyping approach described in this paper addresses those issues as noted below.
In order to correlate the plant&#8217;s dynamic response with its dynamic environment and capture a complete, big picture of complex plant&#8211;environment interactions, both environmental conditions (Figure 4) and plant responses (Supplementary Figure 9B) must be measured continuously. This method enables the measurement of physical changes in the potting medium and atmosphere continuously and simultaneously, alongside plant traits (soil&#8211;plant&#8211;atmosphere continuum, SPAC).
To best predict how plants will behave in the field, it is important to perform the phenotyping process under conditions that are as similar as possible to those found in the field18. We conduct the experiments in a greenhouse under semi-controlled conditions to mimic field conditions as much as possible. One of the most important conditions is the growing or potting medium. Selecting the most suitable potting medium for the gravimetric-system experiment is crucial. It is advisable to choose a soil medium that drains quickly, allows for the rapid achievement of pot capacity and has a highly stable pot capacity, as those features allow for more accurate measurements by the gravimetric system. In addition, the different treatments to be applied in the experiment must also be considered. For example, treatments involving salts, fertilizers or chemicals call for the use of an inert potting medium, preferably one with a low cation-exchange capacity. Drought treatments applied to low-transpiring plant species would work best with potting media with relatively low VWC levels. In contrast, slow drought treatments applied to high-transpiring plants would work best with potting media with relatively high VWC levels. If the roots are required for post-experiment analysis (e.g., root morphology, dry weight, etc.), the use of a medium with relatively low organic matter content (i.e., sand, porous ceramic or perlite) will make it easier to wash the roots without damaging them. For experiments that will continue for longer periods, it is advisable to avoid media that are rich in organic matter, as that organic matter may decompose with time. Please see Table 1 and Table 2 for more detailed information on this topic.
Field phenotyping and greenhouse phenotyping (pre-field) have their own objectives and require different experimental set-ups. Pre-field phenotyping assists the selection of promising candidate genotypes that have a high probability of doing well in the field, to help make field trials more focused and cost-effective. However, pre-field phenotyping involves a number of limitations (e.g., pot effects) that can cause plants to perform differently than they would under field conditions18,27. Small pot size, water loss by evaporation and heating of the lysimeter scales are examples of factors in greenhouse experiments that may lead to pot effects18. The method described here is designed to minimize those potential effects in the following manner:
(a) The pot size is chosen based on the genotype to be examined. The system is capable of supporting various pot sizes (up to 25 L) and irrigation treatments, which enables the examination of any type of crop plant. 
(b) The pots and the lysimeter scales are insulated to prevent heat from being transferred and any warming of the pots. 
(c) This system involves a carefully designed irrigation and drainage system. 
(d) There is a separate controller for each pot, to enable true randomization with self-irrigating and self-monitored treatments. 
(e) The software takes into account the plants&#8217; local VPD in calculating the canopy stomatal conductance. Please see the multiple VPD stations localization in Figure 1J.
This system involves direct physiological measurements at field-like plant densities, which eliminates the need for either large spaces between the plants or moving the plants for image-based phenotyping. This system includes real-time data analysis, as well as the ability to accurately detect the physiological stress point (&#952;) of each plant. This enables the researcher to monitor the plants and make decisions regarding how the experiment is to be conducted and how any samples are to be collected over the course of the experiment. The system&#8217;s easy and simple weight calibration facilitates efficient calibration. High-throughput systems generate massive amounts of data, which present additional data-handling and analytical challenges11,12. The real-time analysis of the big data that is directly fed to the software from the controller is an important step in the translation of data into knowledge14 that has great value for practical decision-making.
This HTP-telemetric physiological phenotyping method might be helpful for conducting greenhouse experiments under close-to-field conditions. The system is able to measure and directly calculate water-related physiological responses of plants to their dynamic environment, while efficiently overcoming most of the problems associated with the pot effect. This system&#8217;s abilities are extremely important in the pre-field phenotyping stage, as they offer the possibility to predict yield penalties during early stages of plant growth.

Ensuring food security for a growing global population under deteriorating environmental conditions is currently one of the major goals of agriculture research1,2,3. The availability of new molecular tools has greatly enhanced crop-improvement programs. However, while genomic tools provide a massive amount of data, the limited understanding of actual phenotypic traits creates a significant knowledge gap. Bridging this gap is one of the greatest challenges facing modern plant science4,5,6. To meet the challenges that arise in the process of crop improvement and minimize the genotype&#8211;phenotype knowledge gap, we must balance the genotypic approach with a phenocentric one7,8.
Recently, various high-throughput phenotyping (HTP) platforms have made possible the nondestructive phenotyping of large plant populations over time and these platforms may help us to reduce the genotype&#8211;phenotype knowledge gap6,8,9,10. HTP screening techniques allow the measurement of traits in massive numbers of plants within a relatively short period of time, thanks to robotics and conveyor belts or gantries used to move the plants or sensors (respectively), as opposed to hand-operated techniques based on gas exchange or photography. Nevertheless, the massive amounts of data produced by HTP systems present additional data-handling and analytical challenges11,12.
Most of these HTP platforms involve the assessment of phenotypic traits through electronic sensors or automated image acquisition13,14. Advanced field phenomics involve the deployment of proximal sensors and imaging technologies in the field, as well as a high-resolution, precise and large-population scale of measurement15. Sensor and image data need to be integrated with other multi-omics data to create a holistic, second-generation phenomic approach16. However, methodological advances in data acquisition, handling and processing are becoming increasingly important, as the challenges of translating sensor information into knowledge have been grossly underestimated during the first years of plant phenomics research13. However, the reliability and accuracy of currently available imaging techniques for in depth phenotyping of dynamic genotype&#8211;environment interactions and plant stress responses are questionable17,18. Moreover, the results from controlled environments are often very different than those observed in the field, especially when it comes to drought-stress phenotyping. This is due to differences in the situation the plants experience in terms of soil volume, soil environment and mechanical impedance due to declining soil moisture during drought stress. Therefore, results from controlled environments are difficult to extrapolate to the field19. Finally, the entry price of image-based HTP systems is very high, not only due to the price of sensors, but also due to the robotics, conveyor belts and gantries, which also require higher standards of growth-facility infrastructure and significant maintenance (many moving parts working in a greenhouse environment).
In this paper, we present an HTP-telemetric phenotyping platform designed to solve many of the problems mentioned above. Telemetry technology enables the automatic measurement and transmission of data from remote source(s) to a receiving station for recording and analysis. Here, we demonstrate a nondestructive HTP-telemetric platform that includes multiple weighing lysimeters (a gravimetric system) and environmental sensors. This system can be used for the collection and immediate calculation (image-analysis is not needed) of a wide range of data, such as whole-plant biomass gain, transpiration rates, stomatal conductance, root fluxes and water-use efficiency (WUE). The real-time analysis of the big data that is directly fed to the software from the controller in the system represents an important step in the translation of data into knowledge14 that has great value for practical decision-making, substantially extending the knowledge that can be acquired from controlled environment phenotyping experiments, in general, and greenhouse studies of drought stress, in particular.
Other advantages of the telemetry platform are its scalability and ease of installation and its minimal growth-facility infrastructure requirements (i.e., it can be easily installed in most growth facilities). Moreover, as this sensor-based system has no moving parts, maintenance costs are relatively low, including both the entry price and long-term maintenance costs. For example, the price of a 20-unit gravimetric system, including the feedback fertigation system for each plant, meteorological station and software, will be similar to the price of one portable gas-exchange system of a leading brand.
Rice (Oryza sativa L.) was used as a model crop and drought was the examined treatment. Rice was chosen as it is a major cereal crop with wide genetic diversity and it is the staple food for over half of the world&#39;s population20. Drought is a major environmental abiotic stress factor that can impair plant growth and development, leading to reduced crop yields21. This crop&#8211;treatment combination was used to demonstrate the platform&#8217;s capabilities and the amount and quality of data that it can produce. For more information regarding the theoretical background for this method, please see 22.

<table>
<tbody>
<tr>
<td>Atmospheric Probes </td>
<td>SpectrumTech/Meter group</td>
<td>3686WD</td>
<td>Watchdog 2475</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>40027</td>
<td>VP4</td>
</tr>
<tr>
<td>Array Randomizer</td>
<td>&nbsp;</td>
<td>None</td>
<td>The software "Array Randomizer" can be used for creating an experimental design of a randomized block design, or fully random design. It was developed to have better control over the random distribution of the experimental samples (plants) in order to normalize the atmospheric microvariation inside the greenhouse.</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>Free download and more information, please click on the following link: https://drive.google.com/open?id=1y4QbTpxRK5Lx430xzu1RFdrlcL8pz_1q</td>
</tr>
<tr>
<td>Cavity trays</td>
<td>Danish size with curved rim for nursery</td>
<td>30162</td>
<td>4X4X7 Cell, 84 cell per tray https://desch.nl/en/products/seed_propagation_trays/danish-size-with-curved-rim-for-nursery~p92</td>
</tr>
<tr>
<td>Coarse sand</td>
<td>Negev Industrial Minerals Ltd., Israel </td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Compost</td>
<td>Tuff Marom Golan, Israel</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Data Analysis software </td>
<td>Plant-Ditech Ltd., Israel</td>
<td>&nbsp;</td>
<td>SPAC Analytics</td>
</tr>
<tr>
<td>Drippers</td>
<td>Netafim</td>
<td>21500-001520</td>
<td>PCJ 8L/h</td>
</tr>
<tr>
<td>Fine sand</td>
<td>Negev Industrial Minerals Ltd., Israel </td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Loamy soil (natural soil)</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Nylon mesh</td>
<td>Not relevant (generic products)</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Operating software </td>
<td>Plant-Ditech Ltd., Israel</td>
<td>&nbsp;</td>
<td>Plantarray Feedback Control (PFC)</td>
</tr>
<tr>
<td>Peat-based soil</td>
<td>Klasmann-Deilmann GmbH, Germany</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Perlite</td>
<td>Agrekal , Israel</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Plantarray 3.0 system</td>
<td>Plant-Ditech Ltd., Israel</td>
<td>SCA400s</td>
<td>Weighing lysimeters</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>PLA300S</td>
<td>Planter unit container</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>CON100</td>
<td>Control unit </td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>part of the planter set</td>
<td>Fiberglass stick</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>part of the planter set</td>
<td>Gasket ring</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>Operating software</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>SPAC Analytics software</td>
</tr>
<tr>
<td>Porous, ceramic, mixed-sized medium</td>
<td>Greens Grade, PROFILE Products LLC., USA</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Porous, ceramic, small-sized medium</td>
<td>Greens Grade, PROFILE Products LLC., USA</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Pots</td>
<td>Not relevant (generic products)</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Soil</td>
<td>Bental 11 by Tuff Marom Golan </td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
<tr>
<td>Soil Probes </td>
<td>Meter group</td>
<td>40567</td>
<td>5TE</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>40636</td>
<td>5TM</td>
</tr>
<tr>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>40478</td>
<td>GS3</td>
</tr>
<tr>
<td>Vermiculite</td>
<td>Agrekal , Israel</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
</tr>
</tbody>
</table>

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.

Watch this Scientific Journal Video about A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of PlantÃ¢â‚¬â€œEnvironment Interactions at JoVE.com

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of PlantÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“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.

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 ...

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