An HPC Framework for Multi-Modal Plant Phenotyping Integrating Controlled Environment and Open Field Studies

Abstract

This paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science. Our framework addresses the unique challenges in high-throughput plant phenotyping (HTP) research, which integrate multi-mode imaging sensors such as RGB, hyperspectral, LiDAR, thermal, and X-ray computed tomography (CT) across indoor and outdoor environments. Our primary objective is to transform raw sensor data into research-ready plant phenotypical traits. This directly supports downstream agricultural research, such as in plant breeding, crop management, etc. The framework is structured into two main phases. The initial data processing occurs on non-HPC systems, utilizing operating system and license-specific software due to the specialized algorithms and knowledge required for each imaging data pipeline. The subsequent HPC phase refines these processed datasets into tabular formats, preparing them for statistical analysis in agronomy, plant science, and bioinformatics. The current implementation of the second phase utilizes a hybrid parallelization model across HPC nodes and threads. However, its performance could be significantly enhanced by implementing more efficient algorithms and optimizing resource allocation. We discuss current bottlenecks, including technical challenges related to sensors, imaging platforms, and computational pipelines, and propose immediate solutions.

Plant phenotyping relevance

HPCを用いて多モーダルセンサーデータから植物形質を抽出する計算フレームワークが研究の中心であり、植物フェノタイピング基盤として該当する。

Code and data availability

The paper describes an HPC phenotyping framework and reports processing results on the authors' own data, but contains no public dataset, image, code, or model availability statement. The only URL-like reference (indexdatabase.de) is a cited external vegetation-index database, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.