Name: Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform
Uploaded: 2024-06-07T13:40:00
Duration: 1 min 51 s
Description: High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions ...

This ADAPT project (Accelerated Development of multiple-stress tolerant Potato) has received funding from the European Union&#39;s Horizon 2020 research and innovation program under grant agreement No GA 2020 862-858. This work was partially supported by the Ministry of Education, Youth and Sports of the Czech Republic with the European Regional Development Fund-Project &#34;SINGING PLANT&#34; (no. CZ.02.1.01/0.0/0.0/16_026/0008446). The Core Facility Plants Sciences of CEITEC MU is acknowledged for its cultivation facility support. We acknowledge Meijer BV for providing the in-vitro cuttings used in this study. We thank Lenka Sochurkova for assisting in the graphical design of Figure 2 and Pavla Homolov&#225; for helping with the preparation of plant material during the experiments at Photon Systems Instruments (PSI) Research Center (Dr&#225;sov, Czech Republic).

Potato Resilience Analysis Under Climate-Related Stresses

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Improved advanced high-resolution imaging tools and computer vision techniques have enabled the rapid development of plant phenotyping to obtain quantitative data from massive plant images in a reproducible manner39. This study aimed to adapt and optimize high throughput image-based methodology using an array of currently available imaging sensors to monitor the dynamic responses of plants under single and combined abiotic stresses. A few critical steps of the applied approach require adjustments, including applying stress and selecting a suitable imaging protocol for the measurements. Using multiple sensors for image acquisition allows the quantification of key phenotypic traits (such as plant growth, photosynthetic efficiency, stomatal regulations, leaf reflectance, etc.). In addition, improves the understanding of how potato plants respond to different abiotic stresses. This is a key prerequisite for accelerating plant breeding projects to develop climate-tolerant genotypes40. The morphological responses to the induced stress depend on the development stage. For example, inducing stress at the stolon or tuber initiation stage inhibits leaf and plant development and limits the number of stolons, thereby reducing the final yield41. However, under unfavorable conditions, plants utilize stress responses as an adaptive response to prevent and repair stress-induced cellular damage42. Plants have adaptive mechanisms to avoid and tolerate stress conditions depending on the severity level43.
To understand the mechanisms of plants, inducing the appropriate duration and intensity of stress and determining the plant responses to stress by using imaging sensors is considered one of the critical steps. When several stresses coincide, the intensity of one stress can overrule the effect of the others depending on the combination, intensity, and duration of the stresses. Thus, the stress effects can add up, or opposing responses can (partially) cancel each other, ultimately resulting in positive or negative effects on plants. The protocol selected in this study was based on previous experience to ensure that sufficient stress levels were applied. For instance, the application of the drought stress was adjusted to a moderate level as in a previous experiment, the response was not different from control treatments at an early stage of stress based on chlorophyll fluorescence imaging. This is due to the occurrence of photorespiration that acts as an alternative sink for electrons in the thylakoid membrane and a protective mechanism for the photosystem II44,45. Under the combined stress response, plant exposure to a mild primary stressor could enhance tolerance to a following stressor, which can have a beneficial or detrimental impact46. In this study, a stronger response was observed under combined stress compared to individual drought stress. By investigating other physiological responses, the results showed an increase in &#916;T (deltaT) under drought as stomata close to avoid excess water loss. In contrast, the reverse response was observed under heat stress where &#916;T was lower compared to control reflecting stomata opening to enhance leaf cooling in accordance with the findings in wheat under combined heat and drought stress20. During waterlogging, the increase of &#916;T due to stomatal closure resulted from oxygen deficiency in the soil and disruption of root water homeostasis, thereby lowering the transpiration stream with an increase in the ABA, a key hormone in water stress responses47.
In plant stress studies, the duration of stress and subsequent recovery treatments is directly proportional to the stress intensity. For instance, moderate drought stress, such as maintaining soil moisture at 20% field capacity (FC), induces reversible phenotypic changes that typically recover after a single day of re-irrigation. In contrast, severe stress conditions like waterlogging result in extensive phenotypic damage, necessitating a longer recovery period. Although standardizing treatment durations is ideal, the inherent variability in stress intensities must be accounted for in experimental design.
The second critical step is to select an appropriate protocol and optimize the settings for each sensor. Chlorophyll fluorescence is a powerful tool in determining the performance of photosynthetic apparatus under stress48. Different chlorophyll fluorescence measuring protocols can be selected with either light or dark-adapted plants depending on the research question and the experimental design49. In this study, the selected protocol (short light response) enables the determination of various traits, including Fv&#39;/Fm&#39;, &#966;PSII, and qL, which indicate the photosynthesis performance under different conditions50. Previous studies showed that the used protocol in high-throughput phenotyping is effective in investigating the photosynthetic efficiency of plants under different applications of stress treatments and discriminating between healthy and stressed plants14,20. Based on the experimental design, it is very critical to consider the duration of the selected protocol when measuring in a high throughput system with a high plant population. Thus, the chlorophyll fluorescence measurement on light-adapted plants using a short-time protocol was selected to discriminate responses under different treatments. Genotype-environment interactions can influence many phenotypic traits, which is critical during measurement12. It is essential to consider that the duration of the measurement should be completed in a short time to minimize the diurnal effect on photosynthetic limitations51.
Thermal IR imaging was used to determine the canopy temperature and understand the stomatal regulation under different treatments52. It is worth mentioning that technological optimization was used where the heating wall was located on the opposite side of the camera, and the wall&#39;s temperature was dynamically controlled and programmable. Thus, adjusting the background heated wall with integrated environmental sensors is necessary to properly select plants from the background by increasing the contrast of the background temperature over the temperature of the imaged object.
Even though image analysis is automated, adjusting RGB thresholding indexes is still required to obtain a proper binary mask in RGB imaging to precisely select plants53. In addition, choosing multiple angles is important for appropriately estimating quantitative parameters, including digital biomass and growth rate. In this study, three angles (0&#176;, 120&#176;, and 240&#176;) on the RGB side view were selected and averaged to calculate the plant volume and relative growth rate accurately.
Depending on the spectral range, many physiological traits can be investigated using hyperspectral imaging54. It is necessary to determine which of the reflectance indices provides the necessary information and shows the response of plants under different conditions14. It is highly demanded in screening for tolerant varieties and plant phenotyping to determine the correlation between the hyperspectral indices and other physiological traits55. In this study, plants under waterlogging treatment showed a pronounced response in the chlorophyll content and photosynthetic efficiency from the VNIR imaging. Moreover, different responses were observed in the water index calculated from SWIR imaging under heat treatments and waterlogging due to different stomatal regulations and water content in the leaves.
Thus, these findings highlight the utility of such an approach after optimizing the settings and the potential of using multiple sensors to find stress traits relevant to climate tolerance. Assessing the dynamics of the responses using multiple imaging sensors can be used as one of the powerful tools in improving breeding programs.

The potential effects of climate change, including the increase in the intensity and frequency of heat waves, flooding, and drought events, have negative impacts on growing crops1. It is important to understand the influence of climate change on crop variability and the consequent fluctuations in annual crop production2. With increasing population and food demand, maintaining the yield of crop plants is a challenge, thereby, finding climate-resilient crops for breeding is urgently required3,4. Potato (Solanum tuberosum L.) is one of the essential food crops that contributes to global food security because of its high nutritional value and increased water use efficiency. However, reduction in growth and yield under unfavorable conditions is a main problem, particularly in the susceptible varieties5,6. Many studies highlighted the importance of investigating alternative approaches to maintain potato crop productivity, including agricultural practices, finding tolerant genotypes, and understanding the impact of stress on the development and yield7,8,9, which is also highly demanded by European potato growers (or farmers)10.
Automated phenotyping platforms, including image-based phenotyping, enable the quantitative analyses of plant structure and function that are essential for selecting relevant traits of interest11,12. High throughput phenotyping is an advanced non-invasive technique to determine various morphological and physiological traits of interest in a reproducible and rapid manner 13. Although phenotype reflects genotypic differences in connection to environmental effects, comparing plants under controlled conditions with stress enables linking the&#160;extensive phenotyping information to a specific (stress) condition14. Image-based phenotyping is essential in describing phenotypic variability, and it is also capable of screening a set of traits across plant development regardless of the population size15. For instance, the measurement of morphological traits, including the shape, size, and color index of leaves using Red-Green-Blue (RGB) imaging sensors, is used to determine plant growth and development. Moreover, measurements of physiological traits, including photosynthetic performance, canopy temperature, and leaf reflectance, are quantified using multiple types of sensors, such as chlorophyll fluorescence, thermal infrared (IR), and hyperspectral imaging16. Recent studies in controlled environments showed the potential of using image-based phenotyping in assessing different mechanisms and physiological responses of plants under abiotic stresses such as heat in potato17, drought in barley18, rice19, and combined drought and heat treatments in wheat20. Even though studying the responses of plants to multiple stress interactions is complex, the findings reveal new insights in understanding plant mechanisms in coping with rapid change in climate conditions21.
Plant physiological and morphological responses are directly influenced by abiotic stress conditions (high temperature, water deficit, and flooding), resulting in yield reduction22. Even though potatoes have a high water use efficiency compared to other crops, water deficit negatively affects the yield quantity and quality due to the shallow root architecture5. Depending on the intensity and duration of drought level, the leaf area index is reduced, and retardation in canopy growth with inhibition of new leaf formation is pronounced during later stages of stress leading to a reduction in the photosynthetic rate23. The threshold level of water is critical with excess water or prolonged drought periods, resulting in a negative effect on plant growth and tuber development due to oxygen limitation, decreased root hydraulic conductivity, and restriction of gas exchange24,25. Moreover, potatoes are sensitive to high temperatures where temperatures above optimum levels result in delayed tuber initiation, growth, and assimilation rates26. When stresses appear in combination, the biochemical regulations and physiological responses differ from individual stress responses, highlighting the necessity of investigating the plant responses to stress combinations27. Combined stresses can result in (even more) severe reductions in plant growth and determinantal effects on reproductive-related traits28. The impact of stress combination depends on the dominancy of each stress over the others, leading to enhanced or suppressed plant response (e.g., drought usually leads to stomata closure while stomata are open to allow cooling of leaf surface under heat stress). However, combined stress research is still emerging, and further investigations are required to understand better the complex regulation mediating plant responses under these conditions29. Thus, this study aims to highlight and recommend a phenotyping protocol using multiple imaging sensors that can be suitable to assess morpho-physiological responses and understand the underlying mechanisms of potato overall performance under single and combined stress treatments. As hypothesized, combining multiple imaging sensors proved to be a valuable tool to characterize the early and later strategies during plant stress response. Optimizing image-based phenotyping protocol will be an interactive tool for plant researchers and breeders to find traits of interest for abiotic stress tolerance.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1.1&#8221; CMOS Sensor with RGB camera</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://psi.cz/</td>
			<td>The sensor delivers a resolution of 4112 &#215; 4168 pixels for side view and 2560 &#215; 1920 pixels for top view. The sensor is extremely sensitive and is a real megapixel CCD replacement and produces sharp, low-noise images</td>
		</tr>
		<tr>
			<td>FluorCam&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>FC1300/8080-15&#160;</td>
			<td>Pulse amplitude modulated (PAM) chlorophyll fluorometer&#160;</td>
		</tr>
		<tr>
			<td>Fluorcam 10 software&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>Version 1.0.0.18106</td>
			<td>For Chlorophyll fluorescence images visualization and analysis</td>
		</tr>
		<tr>
			<td>GigE PSI RGB &#8211; 12.36 Megapixels Camera</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://psi.cz/</td>
			<td>For the side view projections, line scan mode was used with a resolution of 4112 px/line, 200 lines per second. The imaged area from the side view was 1205 &#215; 1005 mm (height &#215; width), while the imaged area from the top view position was 800 &#215; 800 mm.</td>
		</tr>
		<tr>
			<td>Hyperspectral Analyzer software&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>Version 1.0.0.14</td>
			<td>For hyperspectral images visualization and analysis</td>
		</tr>
		<tr>
			<td>Hyperspectral camera HC-900 Series</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://hyperspec.org/products/</td>
			<td>Visible-near-infrared (VNIR) camera 380-900 nm with a spectral resolution of 0.8 nm FWHM</td>
		</tr>
		<tr>
			<td>Hyperspectral camera SWIR1700</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://hyperspec.org/products/</td>
			<td>Short-wavelength infrared camera (SWIR) camera 900 - 1700 nm with a spectral resolution of 2 nm FWHM</td>
		</tr>
		<tr>
			<td>InfraTec thermal camera (VarioCam HEAD 820(800))&#160;</td>
			<td>Flir,&#160; United States</td>
			<td>https://www.infratec.eu/thermography/infrared-camera/variocam-hd-head-800/</td>
			<td>Resolution of 1024 &#215; 768 pixels, thermal sensitivity of &#60; 20 mK and thermal emissivity value set default to 0.95.&#160; with a scanning speed of 30 Hz and each line consisting of 768 pixels. The imaged area was 1205 &#215; 1005 mm (height &#215; width).</td>
		</tr>
		<tr>
			<td>LED panel&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://led-growing-lights.com/products/</td>
			<td>Equipped with 4 &#215; 240 red-orange (618 nm), 120 cool-white LEDs (6500 K) and 240 far-red LEDs (735 nm) distributed equally over an imaging area of 80 &#215; 80 cm</td>
		</tr>
		<tr>
			<td>Light, temperature and relative humidity sensors</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://psi.cz/</td>
			<td>Sensors used to monitor controlled conditions in greenhouse</td>
		</tr>
		<tr>
			<td>MEGASTOP Blue mats&#160;</td>
			<td>Friedola&#160;</td>
			<td>75831</td>
			<td>To cover soil surface</td>
		</tr>
		<tr>
			<td>Morphoanalyzer software&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>Version 1.0.9.8</td>
			<td>For RGB images visualization and analysis and color segmentation analysis</td>
		</tr>
		<tr>
			<td>PlantScreen Data Analyzer software (Version 3.3.17.0)</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://plantphenotyping.com/products/plantscreen-modular-system/</td>
			<td>To visualize and analyze the data from all imaging sensors,&#160; watering-weighing unit and environmental conditions in greenhouse</td>
		</tr>
		<tr>
			<td>PlantScreen Modular system&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://plantphenotyping.com/products/plantscreen-modular-system/</td>
			<td>Type of phenotyping platform</td>
		</tr>
		<tr>
			<td>Plantscreen Scheduler software&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>Version 2.6.8368.25987</td>
			<td>To plan the experiment and set the measuring protocol</td>
		</tr>
		<tr>
			<td>SpectraPen MINI</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://handheld.psi.cz/products/spectrapen-mini/#details</td>
			<td>Light meter to adjust light level on a canopy level</td>
		</tr>
		<tr>
			<td>TOMI-2 high-resolution camera&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://fluorcams.psi.cz/products/handy-fluorcam/</td>
			<td>Resolution of 1360 &#215; 1024 pixels, frame rate 20 fps and 16-bit depth) with a 7-position filter wheel is mounted on a robotic arm positioned in the middle of the multi-color LED light panel with dimensions of 1326 x 1586 mm.&#160;</td>
		</tr>
		<tr>
			<td>Walk-in FytoScope growth chamber&#160;</td>
			<td>PSI, Dr&#225;sov, Czech Republic</td>
			<td>https://growth-chambers.com/products/walk-in-fytoscope-fs-wi/</td>
			<td>Type of chambers used to grow the plant</td>
		</tr>
	</tbody>
</table>

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

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

In this study, automated image-based phenotyping was used to investigate the morphological and physiological responses of potato (cv. Lady Rosetta) under single and combined stress. The applied approach showed the dynamic responses of plants in high spatio-temporal resolution when stress was induced at the tuber initiation stage. To assess the early and late phases of stress, the results were presented as 3 time periods ([0-5 days of phenotyping (DOP)], [6-10 DOP], and [11-15 DOP]) (Figure 1). Until 0 DOP, all plants were grown under control conditions (C), then from 1-5 DOP, where waterlogging stress (W) and heat stress (H) were applied. Thus, the responses were observed as follows: (i) in 0-5 DOP, indicated the initial heat and waterlogging; (ii) in 6-10 DOP, reflected the early drought (D) and combined heat and drought (HD) was observed and (iii) in 11-15 DOP, showed the late heat, drought and combined heat + drought + waterlogging (HDW) stresses. The recovery from waterlogging was observed in 6-10 DOP and 11-15 DOP.
Morphological traits 
RGB imaging was applied to determine the effect of different stresses and combinations on above-ground plant growth. The results in Figure 4 show that heat treatment and waterlogging stress (0-5 DOP) already cause a reduction of plant volume and RGR compared to control. During 6-10 DOP, plant volume and RGR of control plants continuously increased, while under heat, combined heat, drought, and waterlogging, this increase in plant volume was clearly reduced (Figure 4A). As plants are very susceptible to waterlogging stress, a decrease was pronounced in RGR (Figure 4B). During late drought stress (11-15 DOP), where SRWC was maintained at 20%, a clear reduction in RGR was observed compared to the control. However, in the late phase of combined HDW, the application of waterlogging treatment caused an increase in RGR on the last day of stress.
Physiological traits 
The combination of structural and physiological phenotyping was applied to reveal further responses to stress. Using multiple imaging sensors enables the determination of the physiological responses under the early phase of stress. Further analysis of the chlorophyll fluorescence data showed that waterlogging was negatively affecting the photosynthetic efficiency where Fv&#39;/Fm&#39; (Fv/Fm_Lss) decreased dramatically in 0-5 DOP and 6-10 DOP, but a recovering response was observed in 11-15 DOP where Fv&#39;/Fm&#39; slightly increased (Figure 5A). During the late stress phase (11-15 DOP), a reduction of Fv&#39;/Fm&#39; was observed in drought and combined heat and drought. In waterlogged plants, the operating efficiency of plants (QY_Lss aka. &#966;PSII) was significantly lower compared to other treatments in 0-5 DOP and 6-10 DOP but a slight increase at 11-15 DOP, thus indicating plant recovery (Figure 5B). Moreover, the different mechanisms in regulating the efficiency contributing to the protection of PSII were determined by calculating the fraction of open reaction centers in PSII in a light steady-state (qL_Lss) (Figure 5C). Only under drought was an increase in qL observed, probably due to photoinhibition.
These findings were in accordance with IR data that reflected different underlying mechanisms under stresses (Figure 6). An increase in deltaT (&#916;T) was observed in waterlogging, reducing the gas exchange rate. Under late drought and combined heat and drought stresses, an increase in &#916;T was due to stomata closure, considered one of the primary responses to avoid excess water loss. On the other hand, a reduction in &#916;T under heat treatments was observed as stomata open to enhance the transpiration efficiency and cool the leaf surface.
By investigating the hyperspectral data, two parameters were selected from the hyperspectral VNIR data to assess the leaf reflectance indices, including NDVI as an indicator of chlorophyll content and PRI as an indicator of the efficiency of photosynthesis. The results showed a decrease in NDVI and PRI only under waterlogging in connection to the reduction observed in the morphological traits (Figure 7A,B). Furthermore, from the SWIR hyperspectral data used for assessing the water content in the plants, an increase in water index in waterlogging was observed during 0-5 DOP (Figure 7C). However, under heat treatments, an opposite response was observed where the water index was lower than the control. These findings were in accordance with an examination of vegetation from the color segmentation of RGB Top view. The changes in the proportion of hues indicate the stress responses over time (Figure 8). The greening index showed a reduction in the pigment content under drought and combined HDW at the late stress phase and gradual recovery from waterlogging treatment. Thus, using the multiple imaging sensors reflected the correlation of morpho-physiological traits and enabled the assessment of the overall plant performance under abiotic stresses.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66255/66255fig01.jpg" /> 
Figure 1: Timeline of applying the different treatments, including the age of plants in days after transplanting the in vitro cuttings. Day 0 of phenotyping (DOP) was measured under control (C) conditions, and then the different stresses were induced with different durations. From 1-5 DOP waterlogging (W) stress was applied and the initial response of heat treatment (H). The following days 6-10 DOP, where the initial phase of drought stress (D) and combined heat and drought stress (HD) were presented. During 11-15 DOP, the response of plants to the late phase of drought and heat treatments and the application of waterlogging to HD (HDW) for 1 day was reflected. <a href="https://www.jove.com/files/ftp_upload/66255/66255fig01large.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/66255/66255fig02.jpg" /> 
Figure 2: Scheme summarizing the phenotyping protocol and data analysis. (A) Overview of the phenotyping protocol. Plants are transported to the phenotyping system from the controlled conditions at the FS-WI growth chamber (PSI). Plants were light acclimated in the light adaptation chamber for 5 min at 500 &#181;mol.m-2.s-1 before the measurements. Multiple imaging sensors were used to determine morphological and physiological traits, followed by the weighting and watering station. Depending on the treatment, plants were placed back in controlled conditions, either at 22 &#176;C/19 &#176;C or 30 &#176;C/28 &#176;C. (B) Automatic extraction and segmentation of the image processing pipeline from each imaging sensor. <a href="https://www.jove.com/files/ftp_upload/66255/66255fig02large.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/66255/66255fig03.jpg" /> 
Figure 3: Short light protocol overview for chlorophyll fluorescence imaging. The measuring protocol started by turning on cool-white actinic light to measure the steady-state fluorescence in light (Ft_Lss) and then applying a saturation pulse to measure the steady-state maximum fluorescence in light (Fm_Lss). The actinic light was turned off, and the Far-red light was turned on to determine the steady-state minimum fluorescence in light (Fo_Lss). The duration of the protocol was 10 s per plant. <a href="https://www.jove.com/files/ftp_upload/66255/66255fig03large.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/66255/66255fig04.jpg" /> 
Figure 4: RGB imaging used for morphological assessment. (A) Plant volume calculated from the RGB top and side views area. (B) Relative growth rate (RGR) during the tuber initiation stage. The data represent mean values &#177; standard deviation (n = 10). <a href="https://www.jove.com/files/ftp_upload/66255/66255fig04large.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/66255/66255fig05.jpg" /> 
Figure 5: Chlorophyll fluorescence imaging on light-adapted plants. (A) Maximum efficiency of PSII photochemistry of light-adapted sample in light steady-state (Fv/Fm_Lss). (B) Photosystem II quantum yield or operating efficiency of photosystem II in light steady-state (QY_Lss). (C) Fraction of open reaction centers in PSII in light steady-state (oxidized QA) (qL_Lss). The data represent mean values &#177; standard deviation (n = 10). <a href="https://www.jove.com/files/ftp_upload/66255/66255fig05large.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/66255/66255fig06.jpg" /> 
Figure 6: Thermal IR imaging was used to calculate the difference between canopy average temperature extracted from thermal IR images and air temperature (&#916;T).&#160;The data represent mean values &#177; standard deviation (n = 10). <a href="https://www.jove.com/files/ftp_upload/66255/66255fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66255/66255fig07.jpg" /> 
Figure 7: Hyperspectral imaging for determining vegetation indices and water content. (A) Normalized Difference Vegetation Index (NDVI). (B) Photochemical Reflectance Index (PRI) calculated from VNIR imaging. (C) Water index calculated from SWIR imaging. The data represent mean values &#177; standard deviation (n = 10). <a href="https://www.jove.com/files/ftp_upload/66255/66255fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/66255/66255fig08.jpg" /> 
Figure 8: Greening index for plants under different treatments. Image processing is based on the transformation of the original RGB image in a color map consisting of 6 defined hues. The data represent mean values &#177; standard deviation (n = 10). <a href="https://www.jove.com/files/ftp_upload/66255/66255fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Light intensity measured during the days of phenotyping (DOP). The duration of measurements from 9:00 am to 12:35 pm. LI_Buff refers to the median data from 5 light sensors distributed in the greenhouse. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 1.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Relative humidity (RH) measured during the days of phenotyping (DOP). The duration of measurements from 9:00 am to 12:35 pm. RH_Buff refers to the median data from 5 humidity sensors distributed in the greenhouse. RH2 refers to the relative humidity in the adaptation chamber. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 2.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Temperature measured during the days of phenotyping (DOP). The duration of measurements from 9:00 am to 12:35 pm. T_Buff refers to the median data from 5 temperature sensors distributed in the greenhouse. T2 refers to the temperature in the adaptation chamber. T3 refers to the temperature of the heating wall. T4 refers to the temperature in the thermal IR imaging unit. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 3.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: Screenshot from data analyzer software showing the parameters adjusted for plant mask analysis in chlorophyll fluorescence imaging sensors. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 4.jpg" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: Screenshot from data analyzer software showing the parameters adjusted for plant mask analysis in thermal infrared imaging sensors. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 5.jpg" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: Screenshot from data analyzer software showing the parameters adjusted for plant mask analysis in RGB 1-side view imaging sensors. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 6.jpg" target="_blank">Please click here to download this File.</a>
Supplementary Figure 7: Screenshot from data analyzer software showing the parameters adjusted for plant mask analysis in RGB2-top view imaging sensors. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 7.jpg" target="_blank">Please click here to download this File.</a>
Supplementary Figure 8: Screenshot from data analyzer software showing the parameters adjusted for plant mask analysis in VNIR imaging sensors. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 8.jpg" target="_blank">Please click here to download this File.</a>
Supplementary Figure 9: Screenshot from data analyzer software showing the parameters adjusted for plant mask analysis in SWIR imaging sensors. <a href="https://www.jove.com/files/ftp_upload/66255/Supp Figure 9.jpg" target="_blank">Please click here to download this File.</a>

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 ...