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Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Tolerance in Maize


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Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Tolerance in Maize. Mitch Tuinstra

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Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Tolerance in Maize

  1. 1. Next Generation Phenotyping Technologies in Breeding for Abiotic Stress Tolerance in Maize Mitch Tuinstra, Ph.D. Professor of Plant Breeding Scientific Director – Institute for Plant Sciences Purdue University
  2. 2. Micheline Pelletier/Sygma/Corb In the next 50 years, we’re going to have to produce more food than we have in the last 10,000 years. We need to find ways to employ technology and science to increase production to feed a hungry planet. Norman Borlaug Nobel Peace Prize Laureate April 7, 2009 The Challenge …. Food security and sustainability will depend on advances in plant-based agriculture. We need to develop higher-yielding plants that are more nutritious, use water and nutrients more efficiently, and can tolerate more variation in the environment.
  3. 3. Data-Driven Agriculture – Genomics Genome sequencing has transformed that way we do plant breeding and genetic research. State-of-the-Art: BIG DATA Normal Modified brachytic
  4. 4. Data-Driven Agriculture – Phenomics? State-of-the-Art: LITTLE DATA • Phenomics is the study of plant characteristics or traits. • Phenomic analyses are expensive. −$325 per hybrid for a 4 location replicated yield trial with data collected on phenology, grain yield, grain moisture, lodging, and plant population
  5. 5. Physiology Genetic Markers & Genomics Control over Growth Conditions Direct Applicability to Crop Production Growth & Morphology Anatomy Yield Biochemistry Adapted from Bruce et al. (2002) Monitor Crop Characteristics with Greater Accuracy • Study plants in state-of-the-art field and controlled environment facilities
  6. 6. • The CEPF was developed for plant production and high- throughput imaging of diverse crop species. −Conviron Growth House for precise lighting and temperature control −256 plant capacity (0.5-4m) −Argus multi-feed nutrient injection for fertility management −Automated weight-based irrigation system for measuring plant water use Controlled Environment Phenomics Facility (CEPF)
  7. 7. Controlled Environment Phenomics Facility (CEPF) • Plants in the CEPF are grown on an automated conveyer system for daily imaging and phenotyping • RGB and Hyperspectral imaging towers can accommodate crop plants up to 4m tall for shoot- based phenotyping • An X-Ray CT Scanner is being installed to enable below-ground imaging for root-based phenotyping
  8. 8. Greenhouse Imaging System • A greenhouse plant imaging system has been developed at Purdue as a test-bed for new plant sensors and robotics equipment. −Top-view and side-view Middleton Spectral Vision MSV 500 hyperspectral cameras −Automated conveyor belt with space for 108 plants −Flexible imaging booth for sensor testing
  9. 9. Greenhouse Imaging System • Models for Relative Water Content (RWC) and N Content in 8 temperate and tropical maize and sorghum genotypes • Hyperspectral imaging used to model the impacts of water and fertility treatments based on variation in RWC and N content
  10. 10. • RWC and N content measurements are time consuming, labor intensive, and destructive • Important to develop models that can predict responses across genotypes and species • Accurate and nondestructive measurements Greenhouse Imaging System - Why does it matter?
  11. 11. Indiana Corn and Soybean Innovation Center (ICSC) Field-based Phenomics Research • Purdue has invested in a field phenomics research capacity at the Agronomy Farm. ‐ 25,000 ft2 research facility ‐ Plant and seed processing/phenotyping lab ‐ Phenomics tool development workshop ‐ 10 Gb fiber optic connection to high performance computing facility on campus
  12. 12. ICSC Research Facility • 1406 acre research farm with WIFI network in every field • Calibration and ground validation studies for agronomic, morphological, physiological and biomass composition traits Agronomic Traits • Plot stand • Main stem leaf collar height • Main stem diameter base and top collar • Tiller number and height • Leaf number and leaf angle • Leaf size distribution • Total leaf area and leaf area index • Plant biomass • Flowering date • Stem and root lodging Physiological Traits • Canopy temperature • Leaf chlorophyll and nitrogen Composition Traits • Cellulose and hemicellulose content • Lignin composition • Saccharification assessment
  13. 13. ICSC Research Facility – PhenoRover The PhenoRover accommodates up to 11 sensors for data collection with RGB, HS-VNIR, LIDAR, and video. RGB LiDAR-Based Point Cloud Image-Based Point Cloud
  14. 14. ICSC Research Facility – UAV Systems Multiple airframes have been developed for data collection with RGB, LiDAR, HS-VNIR, HS-SWIR, and FLIR sensors RGB Hyperspe ctral SWIR Hyperspec tral VNIR LiDAR DSM
  15. 15. Co-aligned Sensor Data • Geo-referencing methodologies enable precise co- registration of multiple sensors; new opportunities for temporal and multi-scale analyses
  16. 16. Feature Extraction and Machine Learning Automated row/range/plot identification Plot: 4810 1 2 3 4 5 6 7 8 9 10 11 12
  17. 17. Data Visualization Tools Ground Reference Map Hyperspectral Data
  18. 18. Automated Trait Predictions Spacing standard deviation (cm) 34 32 28 29 3632 3233 26302625 Spacing mean (cm) 28 17 8 11 2413 1210 13191111 Date: July 21, 2016 Sensor: Sony Alpha 7R UAV: DJI S1000+ Altitude: 44 m Velocity: 5 m s-1 Overlap: 70% • Measurements of geometric traits: plant height, canopy characteristics … etc. • Trait prediction by machine learning: plant centers and population, canopy architecture, biomass yield … etc.
  19. 19. Predictive Modeling of Biomass Yield Sorghum Biodiversity Panel
  20. 20. Nutritional Deficiencies Map Automated phenotyping • Automated crop phenotyping platforms will enable gene discovery and optimization of crop varieties and production systems for food, feed, fiber, and fuel production. Mobilizing Research for Food Security
  21. 21. Institute for Plant Sciences Yang Yang, Chris Hoagland, Jason Adams, and Richard Westerman, Purdue University Greenhouse Phenotyping Jian Jin and Valerie Cross, Purdue University Field Phenomics Melba Crawford, Ed Delp, Ayman Habib, David Ebert, Keith Cherkauer, Mike Leasure, Clifford Weil, Ali Masjedi, and Neal Carpenter, Purdue University This research was supported by grants and donations from the AgAlumni Seed Company, AgReliant Seed, Corteva AgriSciences, ARPA-E TERRA, Sumitomo, United Sorghum Checkoff, and Purdue University Acknowledgements