AUTHOR=Andeer Peter F. , Zwart Petrus H. , Ushizima Daniela , Noack Marcus M. , Cornmesser Lloyd T. , Vess Thomas M. , Sordo Zineb , Tan Stephen , Zorzi Joseph , Hernandez Chelsea , Novak Vlastimil , Ding Yezhang , Vogel John P. , Bowen Benjamin P. , Sethian James A. , Northen Trent R. 

TITLE=EcoBOT: an AI/ML enabled automated phenotyping capability for model plants

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1633557

DOI=10.3389/fpls.2025.1633557

ISSN=1664-462X

ABSTRACT=IntroductionAdvances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools.MethodsThe EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated.ResultsThe results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement.DiscussionThe findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.