Project Technical Overview and Objectives

Technical Overview:

Purdue University, along with IBM Research and the University of Queensland, will utilize remote sensing platforms to collect data and develop models for automated phenotyping and predictive plant growth. The team will create a system that combines data streams from ground and airborne mobile platforms for high-throughput automated field phenotyping. The team’s custom PhenoRover will be a mobile, ground-based platform that will carry a sensor package capable of measuring numerous plant traits in a large number of research plots in a single day. In addition, the team will use unmanned aerial vehicles (UAVs) equipped with advanced sensors configured to optimize the collection of diverse phenotypic data and complement the data collected from the PhenoRover. Advanced image and signal processing methods will be utilized to extract phenotypic information and develop predictive models for plant growth and development. IBM Research will contribute high-performance computing platforms and advanced machine learning approaches to associate these measurements with genomic information to identify genes controlling sorghum performance. Personnel from the University of Queensland will lend their expertise in crop modelling and phenotyping to create cost-benefit tools for the project’s various phenotyping technologies and model crop trait impacts on biomass and, by extension, biofuel production across various geographies.

Technical Objectives:

  • Optimize UAV and wheel-based platforms and current and emerging remote sensing technologies to acquire relevant data on highly diverse sorghum plant phenotypes in a high-throughput manner.

  • Develop and implement algorithms for segmentation and feature extraction and utilize novel data visualization techniques.

  • Develop predictive models for plant growth and performance through multiple analytical and modeling approaches.

  • Design and employ a sophisticated genetic analysis pipeline to identify genes and traits controlling sorghum performance.

  • Design a user-friendly field phenotyping system to enable breeders and other end users to interact with the data and analytics required to make informed genetic improvement decisions.

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