Automated Discovery of Multicellular Behavior for Optimized Plant Growth and Climate Resilience

Abstract

Developing resilience against climate change and establishing food security will require significant research into the responses of multicellular organisms to their environment. New approaches, such as lab automation, can substantially increase the rate of data collection for organism‐level behavior. This report describes an automated robotic system for studying multicellular organisms to accelerate scientific experimentation. The robot can simultaneously image and deliver chemicals to each organism during multiday experiments, and uses a deep learning model to automatically obtain phenotypic data. This system's abilities are demonstrated by creating a plant growth strategy for food security applications that increases biomass while decreasing nutrient utilization. Furthermore, plant growth is characterized under high salt concentrations to better understand the effects of climate change on freshwater ecosystems. This robotic approach improves lab automation for studying multicellular organisms by increasing experimental throughput, and will enable researchers to improve crop yields under uncertain climates and predict the response of organisms in changing environments.

Plant phenotyping relevance

ロボットによる自動撮像と深層学習による表現型データ取得が研究の中心であり、植物の成長・バイオマスを高スループットに評価する表現型解析プラットフォームである。

Code and data availability

The article describes a robotic duckweed phenotyping platform with a fine-tuned Cellpose model, but no public deposit of the paper's datasets, images, trained models, or analysis code is stated anywhere in the supplied blocks. The only public resource mentioned is the Ara2012/CVPPP dataset, which is cited prior work, a

No evidence-backed public reproduction asset is currently recorded.