Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Unveiling Circumnutation in Pea Plants via Supervised Machine Learning

Version 1 : Received: 11 January 2023 / Approved: 12 January 2023 / Online: 12 January 2023 (09:34:31 CET)

A peer-reviewed article of this Preprint also exists.

Wang, Q.; Barbariol, T.; Susto, G.A.; Bonato, B.; Guerra, S.; Castiello, U. Classifying Circumnutation in Pea Plants via Supervised Machine Learning. Plants 2023, 12, 965. Wang, Q.; Barbariol, T.; Susto, G.A.; Bonato, B.; Guerra, S.; Castiello, U. Classifying Circumnutation in Pea Plants via Supervised Machine Learning. Plants 2023, 12, 965.

Abstract

Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled remains unclear. Here, we use supervised machine learning algorithms to generate models that learn to discriminate between the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features that contribute to the classification tasks. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.

Keywords

plant movement; circumnutation; machine learning; classification; kinematics

Subject

Social Sciences, Behavior Sciences

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