Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses.

Abstract

Advancements in phenotyping technologies, including object imaging, high-throughput monitoring, and soft computing, are pivotal for understanding plant responses to environmental stresses. These technologies enable detailed analyses of morphological, physiological, and structural adaptations under abiotic and biotic stresses, such as drought. Current work using multimodal and multi-perspective image processing methods can capture the essential processes that enhance plant resilience and counteract stress by identifying morphological and biochemical indicators. However, the dynamic and complex nature of plant responses poses multiple challenges for generating precise analytics and descriptors of evolving phenotypes. This work introduces analytics for concurrent imaging, adopting the underlying principle of cosegmentation to create taxonomies for new phenotypes. Here, unidimensional refers to the concurrent analysis of multiple images within a single phenotyping dimension: temporal, modal, or perspective, rather than combining information across dimensions. The proposed unidimensional phenotypes integrate concurrent images within individual temporal, modal, or perspective dimensions to capture dynamic morphological and physiological responses that are not observable with conventional single-image or cumulative metrics. Within a high-throughput imagery production system, these phenotypes enable more nuanced quantification of phenotypic changes, leveraging the strengths of simultaneous image analysis to enhance insight into plant adaptations. This workflow aligns with the investigation of plants’ adaptive strategies under abiotic stress and provides quantitative indicators of plant health under adverse environmental conditions.

Plant phenotyping relevance

植物の同時画像解析とコセグメンテーションに基づく新しい表現型抽出・定量化ワークフローを提案しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's Data Availability Statement explicitly states that the SIMID and SIPID image datasets created and used in this study are publicly available on Zenodo (DOI 10.5281/zenodo.17400167), which is an allowed URL. These are the paper-specific plant phenotyping imagery inputs (buckwheat and sunflower under control/d