High throughput phenotyping using RGB sensor for stress tolerance study.pptx

High throughput phenotyping using RGB sensor for stress tolerance study

1. High throughput phenotyping using RGB sensor for stress tolerance study E-Publication Sudhir Kumar Division of Plant Physiology ICAR-Indian Agricultural Research Institute, New Delhi Akshay S. Sakhare , Plant Physiology Section ICAR-Indian Institute of Rice Research, Hyderabad

2. RGB imaging 2 /17 High-throughput screening (HTS): H undreds of thousands of experimental samples are subjected to simultaneous testing under given conditions Stay green or Greenless : M ost vulnerable parts of the first manifestation of leaf stress (associated with chlorophyll synthesis-degradation) It is related to alterations in photosystems which can be followed up with chlorophyll fluorescence RGB (red, green, blue) imaging: The changes in major photosynthetic parameters during increasing abiotic stress can be monitored By analyzing changes in image timeseries, the plant growth and phenotypic response to abiotic stress can be investigated

3. RGB imaging 3 /17 Minimum Two groups : one treatment and one control (more level of stress can be included) Each plant is imaged individually in three (0◦, 90◦, and 180◦) plane orientations over the time The size and dimensions of the object could be calculated, and all three images can be used to estimate the overall biomass of the plant Plant pixel area is used to calculate digital biomass

4. What to be taken care ? 4 /17 Additional biological replicates may be needed if the stress applied is too subtle. In order to exclude artifacts in the images, the pots must avoid reflections , so opaque colors other than green should be used (e.g., black). When the leaves overlapped, obtain as many snapshots as possible rotating the pot manually in different angles. The combination of views (cameras/angles/zooms/etc.) maximizes the collection of data from one single experiment. Image analysis programs will run from manual to semiautomated to automated, but the researchers will need to determine what tools work best for their questions of interest.

5. RGB imaging in common bean 5 /17 Fig. 1: RGB imaging platform Image analysis Softwares : PlantCV , an open-source image analysis software package Multiple software packages at https:// www.plant-image-analysis.org . FIJI/ImageJ

6. RGB imaging in common bean 6 /17 Fig. 2: Separation of background using image analysis and calculating various parameters

7. Drought stress study in rice 7 /17 Eight image-based parameters using RGB imaging: projected plant area, color, object extent X, object extent Y, convex hull area, compactness, eccentricity, and center of mass Y Water content of the plant is analyzed based on NIR intensity and NIR measurements were used to indicate plant biomass IR thermography for temperature measurements of plants Chlorophyll fluorescence ( ChlF ) is analyzed by quantitative evaluation of the response to abiotic stresses such as salt, heat, and drought (Mehta et al ., 2010) Kim et al . (2022)

8. Drought stress study in rice 8 /17 Images are acquired from a side-view at 0°, 120°, and 240° Plant color was analyzed using Hue channels from 0 to 180 regions : 0–72 (yellow area) - plants grown under drought stress 73–180 (green area) plants grown under normal conditions Plant growth rate is calculated using the projected plant area as an RGB parameter   Kim et al . (2022)

9. Drought stress study in rice 9 /17 NIR imaging for measuring plant water content Because it is difficult to delineate the region of interest using NIR images, response to stress data were obtained by matching RGB and NIR images Image processing to compile RGB and NIR images Plants with higher water content show low NIR intensity Measuring plant temperature IR (infrared) images acquired from 3-week-old plants using microbolometer at approximately 11:00 am, which is the period of high photosynthesis efficiency Kim et al . (2022)

10. Parameters used to analyze growth and drought-related traits 10 /17 Types Description Plant area Plant area was represented by the pixel number of the leaf above the plant. Growth rate was obtained by dividing the difference in the number of pixels between the drought stress treatment intervals by stress treatment time (days) Plant colour Plant color was expressed by extracting the color of the leaf sheath and blade including the stem in Hue. Near-yellow and near-green channels were used for investigating the degree of drought stress Object extent X Object extent X indicates the x-axis length of the rectangle covering the object and was used to measure plant width Object extent Y Object extent Y indicates the y-axis length of the least vertical oriented rectangle covering the object and was used to measure the projected plant height Convex hull area The convex hull area indicates the smallest area enclosed by the outer contour of an object, to estimate the spread of leaves Kim et al . (2022)

11. 11 /17 Types Description Compactness Compactness is the object area divided by the convex hull area. This can express the density of plants including tillers and leaves Eccentricity Eccentricity is a parameter related to the conic section in mathematics. It is defined between 0 and 1, and the shape of the plant is expressed as 0 for a circle and 1 for a line Center of mass Y Center of mass Y indicates the center of gravity of the y-axis. During the drought stress test, it was used to express leaf drying Perimeter The length of the outside boundary of the object NIR intensity The water content of the plant was measured by NIR intensity obtained at the water absorption wavelength of 1450 nm Plant temperature Plant temperature was extracted from infrared image, which had a resolution of 640 × 480 resolution and a spectral range of 7.5–13 μm Parameters used to analyze growth and drought-related traits Kim et al . (2022)

12. 12 /17 Types Description Fluorescence area Fluorescence area refers to the total area of plants that emitted light during fluorescence measurements. Fv /Fm was calculated from this area Fv /Fm Fv /Fm means maximum PSII quantum yield in the dark-adapted state Water use efficiency WUE means the value obtained after subtracting the dry weight before and after the drought stress and dividing it by the total irrigated water Plant water loss rate This was calculated by subtracting the soil water loss rate from the total water loss rate Transpiration rate The transpiration rate is the value obtained by dividing the plant water loss rate by the leaf area Parameters used to analyze growth and drought-related traits Kim et al . (2022)

13. RGB images and different parameters 13 /17 Kim et al . (2022)

14. RGB images and different parameters 14 /17 Kim et al . (2022)

15. Interpretation 15 /17 Eccentricity was higher in osphyb plants than in WT plants, suggesting that osphyb plants had an oval shape because leaf wilting was less visible Object extent X was higher in WT than in osphyb plants ( osphyb are wider) Projected plant area did not increase during re-watering after drought stress in the WT, whereas it increased in osphyb plants during recovery Center of mass Y showed a higher variation of the y-axis coordinates in the WT than in the osphyb because of leaf wilting Kim et al . (2022)

16. Interpretation 16 /17 RGB image-based parameters that most accurately represented drought variance were projected plant area, compactness, convex hull area, and eccentricity In drought stress, the proportion of near-green regions decreased, and that of near-yellow regions increased because of leaf wilting and discoloration of the leaf in the WT compared with the osphyb Kim et al . (2022)

17. Thank you… 17 /17