A paper describing their innovative deep-learning approach, combined with a unique hydroponic device that facilitates in-situ plant imaging, was published June 5 in Scientific Reports.
EGSB recently introduced the latest version of EcoFAB, a novel hydroponic device that facilitates in-situ plant imaging by offering a detailed view of plant root systems. EcoFAB -- developed via a collaboration between EGSB, the DOE Joint Genome Institute , and the Climate & Ecosystem Sciences division at Berkeley Lab -- is part of an automated experimental system designed to perform fabricated ecosystem experiments that enhance data reproducibility.
"In order to analyze the complex plant images from the EcoBOT, we created a new convolutional neural network for semantic segmentation," added Zineb Sordo, a computer systems engineer in AMCR working as a data scientist on the project."Our goal was to design an optimized pipeline that uses prior information about the time series to improve the model's accuracy beyond manual annotations done on a single frame.
This study demonstrates the practical applications of RhizoNet in current research settings, the authors noted, and lays the groundwork for future innovations in sustainable energy solutions as well as carbon-sequestration technology using plants and microbes. The research team is optimistic about the implications of their findings.