The Early Dodder Gets the Host: Decoding the Coiling Patterns of Cuscuta campestris with Automated Image Processing

Abstract

Cuscuta spp., commonly known as dodders, are rootless and leafless stem parasitic plants. Upon germination, Cuscuta starts rotating immediately in a counterclockwise direction (circumnutation) to locate a host plant, creating a seamless vascular connection to steal water and nutrients from its host. In this study, our aim was to elucidate the dynamics of the coiling patterns of Cuscuta , which is an essential step for successful parasitism. Using time-lapse photography, we recorded the circumnutation and coiling movements of C. campestris at different inoculation times on non- living hosts. Subsequent image analyses were facilitated through an in-house Python-based image processing pipeline to detect coiling locations, angles, initiation and completion times, and duration of coiling stages in between. The study revealed that the coiling efficacy of C. campestris varied with the inoculation time of day, showing higher success and fastinitiation in morning than in evening. These observations suggest that Cuscuta , despite lacking leaves and a developed chloroplast, can discern photoperiod changes, significantly determining its parasitic efficiency. The automated image analysis results confirmed the reliability of our Python pipeline by aligning closely with manual annotations. This study provides significant insights into the parasitic strategies of C. campestris and demonstrates the potential of integrating computational image analysis in plant biology for exploring complex plant behaviors. Furthermore, this method provides an efficient tool for investigating plant movement dynamics, laying the foundation for future studies on mitigating the economic impacts of parasitic plants.

Plant phenotyping relevance

Cuscutaのコイリングや運動動態を画像から自動抽出するPythonパイプラインの開発・信頼性検証が研究の中心であり、植物状態・形態動態のフェノタイピング手法に該当する。

Code and data availability

The paper's data availability statement points to two public, paper-specific assets: the authors' Python/Jupyter notebook phenotyping pipeline on GitHub and the time-lapse video datasets used in the study hosted as a YouTube playlist. Both are directly tied to this paper's Cuscuta coiling phenotyping measurements and分析