Integration of machine learning and 3D plant biophysical models for acceleration of agricultural system design, management, and crop improvement

Brian N Bailey @ UC Davis

Abstract:

Helios is a flexible, high-performance plant modeling framework that explicitly represents the 3D geometry and interactions between plants and their mircoenvironment, and is implemented as an open-source C++ API. The core engine of Helios manages geometry and data associated with 3D models, and acts as the model coupler. Model and utility routines are implemented as plug-ins to the core engine, which include, e.g., models of radiation transfer, energy balance (for computing temperature and water use), photosynthesis, stomatal conductance, soil water transport, in addition to utilities for processing aerial and terrestrial LiDAR data, and procedurally constructing randomized 3D plant geometries. Many of these plug-ins utilize graphics processing units (GPUs) to perform computations in parallel to maximize the modeled range of scales and level of physical realism.

This presentation will highlight a number of ongoing applications of Helios relevant to agricultural production and breeding, including:

• Generation of auto-annotated simulated sensor data (e.g., RGB-D, multispectral, thermal, LiDAR) for efficient training of machine learning models for physiologically-based phenotyping

• 3D computer-aided design and management of efficient and sustainable agricultural systems

• An irrigation system CAD tool integrating irrigation hydraulics, soil hydrology, and plant demand

• Computer-aided mechanical harvester design with explicit 3D representation of harvester-plant-fruit interactions

• Novel algorithms and implementations for processing LiDAR data into useful 3D model inputs and traits

Bio:

Dr. Brian Bailey brings an interdisciplinary research background that spans the fields of engineering, computer science, and plant biology. He received his PhD in Mechanical Engineering in 2015, before working at the USDA-ARS in Corvallis, OR as a Research Engineer. Since joining the Department of Plant Sciences at the University of California, Davis in 2016, his research program has focused on merging his interests in biophysics and plant physiology in order to develop next-generation 3D plant models for computer-aided design, management, and breeding of efficient agricultural systems. Applications of these modeling tools have focused on canopy design, irrigation design and management, disease management, and integration with machine learning models for high-throughput physiologically-based phenotyping.