Phenomics assisted breeding in crop improvement

As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.

1. Phenomics Assisted Breeding: An emerging way to accelerate crop improvement Master’s Credit seminar Department of Genetics & Plant Breeding

2. Seminar Outline

3. Introduction Phenotypic response & performance Precision analysis of phenotypes for dissection of plant response Growing population Climate change Hastening of plant breeding efforts Plant breeding gain & productivity Kumar et al. 2020

4. W. Johansson

5. Traditional phenotyping Time consuming Labor intensive Spatial and temporal flexibility Error prone Not precise Failure to capture dynamic G×E interaction on organism wide scale leading to genotype- phenotype gap Advanced breeding methodologies require deep phenotypic data The traditional phenotyping is done by visual screening which cannot be useful for physiological and biochemical characteristics . Conventional Phenotyping

6. Phenomics Phenomics is defined as science and action for quantifying plant responses to external stimuli in a way that data generated can help in the precise identification of the genes responsible for them. Jangra et al. 2021

7. Need of Phenomics Mir et al. 2019

8. Phenomics: A multidisciplinary approach Zhao 2019

9. Forward & Reverse phenomics Kumar et al . 2015

10. Steps involved in High- throughput phenomics Rahman et al. 2015

11. Imaging Techniques in Phenomics Li et al. 2014

12. Visible light imaging Digital imaging in the visible wavelength . Digital image is captured using silicon or RGB sensors in a wavelength range of 400-750 nm. 2D images can be used to analyse numerous phenotypic traits. Traits: Growth, deficiencies, Stress, senescence, number of total spikelets

13. Leaf Counting in Paddy Vishal et al. (2020)

14. 2. IR Thermal Imaging Spectral range of thermal cameras: 3 to 14 μm and wavelengths of 3–5 μm and 7–14 μm are most commonly used. Liu et al (2018)

15. 3. Near Infrared imaging The reflectance is high in the NIR region, between 800 and 1300 nm. The reflectance declines beyond 1,300 nm due to absorption by tissue water: there is formation of strong water absorbing bands . This imaging technique estimate the relative water content . It is also used to study the carbohydrate content of leaves, starch, protein etc.

16. 4. Chlorophyll Fluorescence Imaging The light energy absorbed by green plants meet one of the following three fates: One part of this energy is used for electron transport and carbon assimilation. Another fraction is dissipated as the heat Finally remained of light energy is emitted as the fluorescence Fv/Fm is the direct indicator of photosynthetic activity.

17. Chlorophyll fluorescence imaging for disease resistance A: Visible image B: Fv/Fm image obtained by chlorophyll fluorescence imaging. Rousseau et al. 2018

18. 5. Spectral Imaging The range of wavelength in the spectral domain can be from 400 to 2500 nm. Depending on the part of the electromagnetic spectrum that is used to analyze reflectance, different matter can have different patterns of reflectance. A hyperspectral camera measures all the wavelengths of light that are either reflected or absorbed by a plant Kuska et al. 2018

19. 6. Magnetic Resonance Imaging MRI uses a combination of magnetic field and radio waves to take images and is most commonly used for imaging plant roots MRI allow the 3D geometry of the roots to be viewed just as if the plants grown in the soil . Applications: Belowground symptoms caused by cyst nematode and Rhizoctonia solani (Sugar beet ) Hillnhutter et al. ( 2017 )

20. Other imaging techniques Technique Parameters phenotyped Plant species phenotyped 3D imaging Canopy and shoot structure, root architecture, plant height etc. Soybean, maize, pepper, rye Laser imaging Shoot, root and canopy structure, shoot biomass, plant height, leaf angle distribution Soybean, wheat, maize, sugarbeet , barley, rye Positron emission thermography Water transport, flow speed, etc. Barley, sugarbeet Computed tomography Grain quality , tillers , etc. Wheat, rice Jangra et al. 2021, Phenomics

21. Material and Methods 20 wild type plants and 20 osphyb plants were grown under normal and drought stress conditions Image based parameters and analysing tools RGB imaging and extracting image based parameters NIR imaging for Plant water content IR imaging to assess Plant temperature Fluorescence imaging for Photosynthetic efficiency Kim et al. 2020, Planta

22. Results The results indicated that Wild type plants showed significant differences under normal and drought stress conditions in terms of temperature, photosynthesis and water content. These methods can detect the difference between tolerant and susceptible plants .

23. Plant phenomics platforms

24. Controlled environment phenomics platforms LemnaTec scanalazyer 3D Phenovator Phenoscope GrowScreen

25. Field based phenomics Xu and Li 2022

26. Phenomobile It is a modified golf buggy that moves through a field of plants, taking measurements from three rows of plants at the same time. Ladybird Autonomous unmanned ground vehicle robot for row crop phenotyping and coupled with a data processing framework Underwood et al. 2017

27. Phenocopter Take images from a few centimetres above the ground to a height of up to 80 m. Equipped with a computer, a GPS, and infrared cameras. Identify relative difference in canopy temperatures Blimp Distance above the ground: 30 to 100m This allows many plants to be measured at same point of time. It is held in place by a rope.

28. Drones Unmanned aerial vehicle. Works along with onboard sensors & GPS. Includes a ground based controller and a system of communications with the UAV. Small in size Can be used regardless of cloud cover. Take high-resolution pictures of trials to derive traits such as NDVI, digital ground cover, flowering detection, plant leaf surface, maturity etc.

29. Other Phenotypic platforms Name Description PHENOPSIS Allows a culture of approx. 200-500 Arabidopsis plants TraitMill Developed by Crop Design. Enables large-scale transgenesis and automated high resolution phenotypic evaluation PHENODYN Monitors plant growth and transpiration rate with stressful environmental condition Plant Scan Provides non- invasive analysis of plant structure, morphology and function Rane et al. 2017

30. Objective: To assess the impact of drought on winter wheat growth based on multi-temporal UAS-based canopy features. Bhandari et al. 2021, Remote sensing

31. Materials & methods 21 Genotypes were grown in 2018 and 22 were grown in 2019 under both irrigated and dryland conditions Unmanned aerial vehicle, Quadcopter equipped with multispectral sensors Traits studied: Canopy cover and canopy height

32. Results Canopy height(CH) Canopy cover(CC)

33. Image Analysis Hilli 2022

34. Analysis software Name Description ImageJ Used to process and measure a large quantity of phenotypic traits. Generates histogram and profile plots HTPheno A high throughput plant phenotyping image analysis pipeline implemented as a plug-in for ImageJ. Performs calibration and image processing functions. Rosette tracker Performs calibration, image segmentation, rosette detection and plant growth analysis. Zhang et al. 2018

35. Name Description PANorama Measures multiple architectural and branching phenotypes. Phenophyte A web based application which measures area- related phenotypic traits. Leaf Analyzer An automated software for rapid and large-scale analyses of leaf shape variation. LAMINA Automated leaves image analysis tool which measures a variety of characteristics related to leaf shape and size

36. Mishra et al. 2021

37. Results Visual image Patch by Patch segmented images converted into combined input visual image Spikes region only

38. The final output of Web- SpikeSegNet after detection and counting of spikes from the visual images of wheat plant. Continued…

39. International Plant Phenomics Network (IPPN) Regional and National Partners

40. Phenomics facilities in India

41. Applications

42. Concerns Data storage Huge storage facility is required Correlation between phenomics and manually collected data High correlation is needed BIG DATA analysis- Lack of trained manpower Cost Should be affordable and cost effective

43. Conclusions Phenomics is a powerful, non- destructive, efficient and effective tool of crop phenotyping. Automation and robotics, new sensors and imaging technologies have provided an opportunity for high throughput phenotyping platforms. The cost-effective, user-friendly technology attached with simple image processing software is necessary to identify the climate-resilient varieties.

44. “With technology we can achieve the unimaginable”…