HIGH THROUGHPUT PHENOTYPING FOR SCREENING DROUGHT TOLETRANT GENOTYPES.pptx

HIGH THROUGHPUT PHENOTYPING FOR SCREENING DROUGHT TOLERANT GENOTYPES

1. HIGH THROUGHPUT PHENOTYPING FOR SCREENING DROUGHT TOLETRANT GENOTYPES Sudhir Kumar Division of Plant Physiology ICAR-Indian Agricultural Research Institute, New Delhi E-Publication Akshay S. Sakhare , Plant Physiology Section ICAR-Indian Institute of Rice Research, Hyderabad

2. INTRODUCTION Field screening at different moisture regimes provides an empirical way of evaluating germplasm and elite progenies for drought at desired crop stage. Recent advances in imaging technologies have allowed the estimation of biomass and growth parameters nondestructively and rapidly. The use of image-based technologies (RGB, multispectral and hyperspectral sensors) enables the quantification of crop responses to stress in both controlled environmental conditions and field trials.

3. HIGH THROUGHPUT PHENOTYPING FOR SCREENING DROUGHT TOLETRANT GENOTYPES: Deikman et al ., 2012

4. Phenotyping sensors across the electromagnetic spectrum showing wavelengths and frequencies: Al-Tamimi et al ., 2022

5. VISIBLE IMAGING (RGB): Visible imaging using RGB camera is the most widely used system to measure the morphological properties (whole image or partial image of plant, plant structure, shoot biomass, leaf density, leaf area, height, and color of plants) due to its cost-effectiveness and ease of installation. Unlike consumer cameras, RGB cameras contain an infrared blocking filter (VIS camera) that detects light wavelengths between 400 and 700 nm. The VIS camera uses red, green, and blue color sensors to measure the color of each pixel. The pixel values of plants identified by the image processing algorithm are utilized to collect morphological or color information Kim et al., 2020

6. Changes in plant biomass under drought stress can be identified and analyzed pixel by-pixel using RGB images. Biomass content inference using pixel counts has shown a high correlation in various crops. Therefore, RGB images are helpful in predicting changes in the growth rates of plants under drought stress, making it possible to analyze whole plants or specific parts of a plant. In addition, color analysis can confirm leaf wilting and chlorophyll deficiency due to drought stress.

7. The greenness of the leaves was estimated by converting the images from the RGB to the Hue Saturation Intensity (HIS) color management system. In field experiments, phenotypic analysis is performed by attaching sensors to an unmanned aerial vehicle (UAV) to cover large area and to acquire high-resolution color space information quickly. Based on that information, can collect various vegetation indices. Bhandari et al . computed canopy features such as canopy cover and canopy height using UAV in wheat. These study shows high-throughput UAV data can be used to monitor the drought effects on wheat growth and productivity.

8. RGB images at various growth stages: (C) seedling (Z14–19), (D) tillering (Z20–30), (E) stem elongation (Z30–39), (F) booting (Z40–49), and grain development stages (Z61–85) (G) GD1 and (H) GD2: Kumar et al ., 2020

9. Ge et al . extracted plant pixels from RGB images in two levels of water application and used them to establish the correlation with destructively measured shoot fresh weight, dry weight, and leaf area. Plant projected area extracted from side view of maize RGB images can be accurately related to destructively measured plant shoot fresh weight, shoot dry weight, and leaf area at the early growth stage . Campbell et al . estimated daily shoot biomass and soil water content using HTP platform and modeled shoot growth. After that, several candidate genes were identified by combining a genome-enabled growth model.

10. Case study Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress Jiang et al ., 2021 Objective: RGB image acquisition by an unmanned aerial vehicle (UAV) was utilized to quantify the dynamic drought response of a rice population under field conditions. Traits studied: Highly correlated phenotypic traits including UAV-based leaf-rolling score ( LRS_uav ), plant water content ( PWC_uav ) and a new composite trait, drought resistance index by UAV ( DRI_uav ).

11. Visualization of the dynamic UAV-based leaf-rolling score ( LRS_uav ) change in the 2018 rice :

12. Heat map of LRS_uav for 120 accessions:

13. Comparison of dynamic traits for resistant and sensitive accessions: (a) Leaf-rolling score by UAV ( LRS_uav ); (b) relative plant water content by UAV ( PWC_uav_R ); (c) drought resistance index by UAV ( DRI_uav ) LRS_uav is an effective trait to discriminate the resistant and sensitive accessions.

14. Analysis of plant traits in response to drought stress using plant phenomics technology Kim et al ., 2020

15. Image-based parameters of drought-related traits determined using RGB imaging: a- Projected plant area b-plant color c-object extent X d-object extent Y e-convex hull area f- compactness g-eccentricity h-center of mass Y Kim et al ., 2020

16. RGB HTP analyses for control and drought-stressed plants in the period 55–147 DAS, measured using a Lemnatec Scanalyzer 3D The acquired RGB Images of a representative durum wheat genotype, showing the effects of drought stress on plant growth. The drought stress interval is indicated with dashed box Danzi et al .,2018

17. Two traits were considered: Digital biovolume , a measure based on imaging techniques in the RGB domain, and Water Use Efficiency index as calculated semi-automatically on the basis of evaporation measurements. Phenotyping Analysis: Images in the RGB domain (white light) for HTP were captured every other day, by using a Scanalyzer 3D system RGB module of the 3D Scanalyzer . The imaging involving three mutually orthogonal vantage points was used to evaluate morphometric parameters of the plant, such as height, width, or biomass ( Petrozza et al., 2014).

18. The digital biovolume (DB), was calculated from the three orthogonal images of the same plant according to the formula- Xpixelsideview0 ◦ + Xpixelsideview90◦ + logX pixeltopview /3 It is assumed to be proportional to the aerial mass of the plant ( Poiré et al., 2014) Wheat plants were monitored applying image acquisition at 2-day intervals from 55 DAS up to 147 DAS for a total of 92 days. The RGB imaging index DB was used to monitor the phenotypic response to drought stress in wheat. Danzi et al .,2018

19. The evaluation of the DB in control and drought stressed plants, showed that this index is significantly affected in plants subjected to drought starting at 8 days after the imposition of the stress, then it remained quite steady for 12 days, to drop significantly in the last 12 days of the withholding of water (124– 147 DAS) reaching the minimum value recorded. The highest difference observed between the treatments was at 139 DAS (F351.417; P < 0.001) Further statistical analyses, such as regression analysis, support the efficacy of high throughput phenotyping in monitoring plants WUE, and highlight how DB can be used as an index to perform quality testing under drought conditions.

20. Trait’s analysed for drought tolerance using rgb : RGB allows objective quantification of colour change and changes in leaf structure Leaf curving, which is a drought adaptation by reducing transpiration, has been quantified by imaging maize in the late afternoon (rolled leaves) and at pre-dawn the following day (unfolded leaves) and comparing the two images 45 days after planting. High resolution color picture (RGB picture), taken from the top and two side views, is used to determine the projected shoot area of the plant. It serves as a measure for biomass. RGB (Red Green Blue) images are processed to estimate projected plant area, which are correlated with plant shoot fresh weight (FW), dry weight (DW) and leaf area. Estimated plant FW and DW, along with pot weights, are used to derive daily plant water consumption and water use efficiency (WUE) of the individual plants

21. Limitations: Visible image provides only morphological information, and it is challenging to separate leaves and soil of similar colors during the image segmentation process. Therefore, RGB sensors are mainly used as base sensors in combination with additional sensors of other types. In field experiments, since most RGB sensors are attached to UAVs, the acquired images are often limited to the upper image. Therefore, such images are used for vegetation index analysis, such as leaf area index, canopy and chlorophyll content rather than accurate biomass analysis

22. REFERENCES: Gracia -Romero, A., Kefauver, S.C., Fernandez-Gallego, J.A., Vergara-Díaz, O., Nieto- Taladriz , M.T. and Araus , J.L., 2019. UAV and ground image-based phenotyping: a proof of concept with durum wheat.  Remote Sensing ,  11 (10), p.1244. Danzi, D., Briglia , N., Petrozza , A., Summerer , S., Povero , G., Stivaletta , A., Cellini, F., Pignone , D., De Paola, D. and Janni , M., 2019. Can high throughput phenotyping help food security in the mediterranean area?. Frontiers in Plant Science, 10, p.15. Kumar, D., Kushwaha, S., Delvento , C., Liatukas , Ž., Vivekanand, V., Svensson , J.T., Henriksson , T., Brazauskas , G. and Chawade , A., 2020. Affordable phenotyping of winter Kim, J., Kim, K.S., Kim, Y. and Chung, Y.S., 2020. A short review: Comparisons of high-throughput phenotyping methods for detecting drought tolerance. Scientia Agricola, 78. wheat under field and controlled conditions for drought tolerance. Agronomy, 10(6), p.882. Danzi, D., Briglia , N., Petrozza , A., Summerer , S., Povero , G., Stivaletta , A., Cellini, F., Pignone , D., De Paola, D. and Janni , M., 2019. Can high throughput phenotyping help food security in the mediterranean area?. Frontiers in Plant Science, 10, p.15. Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z., Liu, Q., Zhao, H., Yang, W., Xiong , L. and Zhang, J., 2021. Combining UAV‐RGB high‐throughput field phenotyping and genome‐wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytologist, 232(1), pp.440-455. Kim, S.L., Kim, N., Lee, H., Lee, E., Cheon , K.S., Kim, M., Baek , J., Choi, I., Ji, H., Yoon, I.S. and Jung, K.H., 2020. High-throughput phenotyping platform for analyzing drought tolerance in rice. Planta, 252(3), pp.1-18.