Machine learning-driven root plant phenotyping using imaging solution for space farming applications

Abstract

Producing food is one of the challenges in space exploration due to limited storage capacity and long travel duration. Extreme environmental conditions such as microgravity, elevated CO2 levels, irregular light exposure, and fluctuating air temperatures pose significant challenges to conventional plant growth and make it susceptible to stress, particularly in root systems, which struggle to absorb water and nutrients efficiently. This study will focus on root phenotyping of the plants (wheat and lettuce) grown in a near-space environment, and the impact of environmental stressors on the plants using image-based technology will be carried out. A specialized growth chamber is designed, incorporating three automated multi-modal imaging systems (MIS): Visible and Near-Infrared (VNIR) wavelength range (400-1000 nm), Micro CT Scan, and RGB cameras used to observe the impact of stress on microgravity on plants. Machine learning and deep learning techniques were also employed to optimize the discriminant classifier within the multi-modal imaging system. Through comparative analysis of these imaging techniques coupled with artificial intelligence techniques, this study aims to deepen our understanding of how microgravity and other space-induced factors affect root systems. This work will also present the challenges and potential faced that can contribute valuable insights for plant growth under space conditions.

Plant phenotyping relevance

根の画像ベース表現型計測システムを開発・比較し、機械学習による解析も行うことが中心であるため、植物フェノタイピング手法論文として含める。

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