Developed by Berkeley Lab researchers, RhizoNet is a new computational tool that harnesses the power of AI to transform how we study plant roots, offering new insights into root behavior under various environmental conditions
With the climate rapidly altering, horticulturists around the world have been searching for solutions to maintain plant quality despite the changing environmental conditions.
Recently, scientists from Lawrence Berkeley National Laboratory’s Applied Mathematics and Computational Research (AMCR) and Environmental Genomics and Systems Biology (EGSB) Divisions have developed RhizoNet, an AI-driven tool for analyzing plant roots.
The new artificial intelligence tool has been described in a statement by the scientists as a state-of-the-art deep learning architecture, specifically a Residual U-Net, designed to automate the segmentation of plant root biomass from color scans.
It revolutionizes root image analysis, offering precise insights into root behavior under various environmental conditions.
Precision tracking of root growth and biomass
Unlike conventional methods which are usually labor-intensive and prone to errors, the new tool enables researchers to track root growth and biomass with precision as per the statement.
In simple terms, RhizoNet automatically interprets images of plant roots which makes it more effortless and faster to comprehend the workings of root growth and how they respond to different conditions.
This tool comes at a critical time due to the looming consequences of climate change. This solution could help scientists develop crops that can thrive in changing environments, ensuring food security and sustainability as weather patterns become more unpredictable.
The tool employs an AI technology called a convolutional neural network twitch allows the automatic analysis of plant roots.
It paved the way for scientists to accurately measure root growth and biomass, making it much easier and faster to study plant roots in the lab.
As per the statement, this advanced tool is a big step towards creating fully automated labs that can conduct experiments with minimal human intervention.
RhizoNet to standardize root segmentation
Daniela Ushizima from Berkeley Lab, also the lead investigator of the AI-driven software, explained that the capability of RhizoNet to standardize root segmentation and phenotyping represents a substantial advancement in the systematic and accelerated analysis of thousands of images.
“This innovation is instrumental in our ongoing efforts to enhance the precision in capturing root growth dynamics under diverse plant conditions.”
The tool was operated in EcoFAB, a hydroponic system developed by EGSB, enabling detailed imaging of plant roots subjected to specific nutritional treatments.
The study found that RhizoNet could accurately identify and measure plant roots in images, even in complex and noisy backgrounds.
The AI tool precisely distinguished roots from other objects and artifacts, outperforming manual methods. By using smaller image patches, the model captured fine root details better and improved its accuracy.
When the researchers compared RhizoNet’s automated measurements of root biomass to actual measurements, they found a strong correlation, confirming the tool’s reliability.
Since scientists could pinpoint the concerns with plant roots, RhizoNet was able to aid in the study of plant growth and resilience under various environmental conditions.
AI tool analyzed a small grass species
The statement noted that scientists demonstrated the effectiveness of the tool by processing root scans of Brachypodium distachyon – a small grass species of plants subjected to different nutrient deprivation conditions over about five weeks.
The images of the plant were captured every three to seven days which presented valuable data and allowed scientists to comprehend how roots can be adapted to the varying conditions.
“We’ve made a lot of progress in reducing the manual work involved in plant cultivation experiments with the EcoBOT, and now RhizoNet is reducing the manual work involved in analyzing the data generated,” stated Peter Andeer, a research scientist in EGSB and a lead developer of EcoBOT.
“This increases our throughput and moves us toward the goal of self-driving labs,” he added.
Ushizima further explained that EcoBOT is capable of collecting images automatically, but it was unable to determine how the plant responds to different environmental changes alive or not or growing or not,” Ushizima explained.
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“By measuring the roots with RhizoNet, we capture detailed data on root biomass and growth not solely to determine plant vitality but to provide comprehensive, quantitative insights that are not readily observable through conventional means. After training the model, it can be reused for multiple experiments (unseen plants).”
The study was published earlier yesterday [June 21, 2024] in the journal –Scientific Reports.
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Shubhangi Dua As a quirky and imaginative multi-media journalist with a Masters in Magazine Journalism, I'm always cooking up fresh ideas and finding innovative ways to tell stories. I've dabbled in various realms of media, from wielding a pen as a writer to capturing moments as a photographer, and even strategizing on social media. With my creative spirit and eye for detail, I've worked across the dynamic landscape of multimedia journalism and written about sports, lifestyle, art, culture, health and wellbeing at Further Magazine, Alt.Cardiff and The Hindu. I'm on a mission to create a media landscape that's as diverse as a spotify playlist. From India to Wales and now England, my journey has been filled with adventures that inspire my paintings, cooking, and writing.
