Plant high-throughput phenotyping using photogrammetry and 3D modeling techniques

Abstract

Plant phenotyping has been studied for decades for understanding the relationship between plant genotype, phenotype, and the surrounding environment. Improved accuracy and efficiency in plant phenotyping is a critical factor in expediting plant breeding and the selection process. In the past, plant phenotypic traits were extracted using invasive and destructive sampling methods and manual measurements, which were time-consuming, labor-intensive, and cost-inefficient. More importantly, the accuracy and consistency of manual methods can be highly variable. In recent years, however, photogrammetry and 3D modeling techniques have been introduced to extract plant phenotypic traits, but no cost-efficient methods using these two techniques have yet been developed for large-scale plant phenotyping studies. High-throughput 3D modeling techniques in plant biology and agriculture are still in the developmental stages, but it is believed that the temporal and spatial resolutions of these systems are well matched to many plant phenotyping needs. Such technology can be used to help rapid phenotypic trait extraction aid crop genotype selection, leading to improvements in crop yield. In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems, image processing, and 3D reconstruction algorithms to build 2D mosaicked orthophotos and 3D plant models. Chamber-based and ground-level field implementations can be used to measure phenotypic traits such as leaf length, rosette area in 2D and 3D, plant nastic movement, and diurnal cycles. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.