GREENTRIBE: A modular multi-sensor high-throughput plant phenotyping framework for indoor facilities

Abstract

Indoor high-throughput plant phenotyping (HTPP) platforms require flexible, interoperable architectures to support reproducible trait acquisition across changing controlled-environment experiments. This paper presents GREENTRIBE, a modular multi-sensor indoor HTPP framework that integrates sensing, robotic coordination, data communication, semantic data management, and crop modelling within a unified phenotyping pipeline. Rather than treating these elements as independent modules, GREENTRIBE connects distributed sensor acquisition, robot-assisted operation, lightweight message exchange, ontology-based data organization, and process-based model assimilation through a layered architecture implemented with CAN, ROS 2, MQTT, OpenSILEX, and STICS. The platform combines a multiscale sensing network with a sensor-independent communication protocol, enabling heterogeneous imaging, environmental, and plant-monitoring devices to be configured within indoor experimental designs. To support traceable and reusable workflows, GREENTRIBE implements an ontology-driven data management layer aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) metadata standards. The architecture further links computer vision and artificial intelligence pipelines with the STICS crop model, allowing multimodal observations to be transformed into biologically interpretable phenotypic information under explicit genotype, environment, and management contexts. Platform validation demonstrated reliable communication, efficient multimodal data handling, and standardized metadata management across the sensing-to-information pipeline. Under the configured acquisition schedule, GREENTRIBE achieved a maximum full data cycle of approximately 200 ms, no measurable losses up to the CAN master, high metadata completeness, and support for 1704 scheduled daily acquisition events from seven devices. Overall, GREENTRIBE provides a modular and interoperable indoor phenotyping framework for reproducible experiments and standardized multimodal phenotypic data generation.

Plant phenotyping relevance

植物フェノタイピングのための多センサーHTPP基盤を中心に開発・統合し、通信性能、データ処理、メタデータ管理を検証しているため。

Code and data availability

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