AI is quietly rewiring how scientists study and breed plants
Mehrotra S, Mishra S, Srivastava V, Yadav D, Singh V.
Crispr
The disease-resistant tomato variety at your garden center or the drought-tolerant grass seed at the hardware store increasingly got there faster because a computer model, not just a breeder's eye, flagged which plants to test first.
Researchers are using computer programs, sensors, and robots to study plants in ways that used to take years of hands-on work. These tools can spot which plant genes lead to better traits, monitor crop health in real time, and even help design new ways to edit genes or grow plants from tissue samples. The catch is that not every lab or farm has the data, equipment, or training to use these tools yet, so the benefits aren't spreading evenly.
Key Findings
AI and machine learning now support genotype-phenotype matching, precision farming, tissue culture optimization, genome editing, and biosynthetic pathway design across plant science.
Integration with IoT sensors, robotics, microfluidics, and digital twins enables real-time monitoring and modeling of plant growth.
Major barriers to wider adoption include inconsistent data standards, high infrastructure costs, poor model interpretability, and gaps in digital literacy among researchers and growers.
chevron_right Technical Summary
Plant scientists are folding artificial intelligence into nearly every part of their work, from reading plant genes to tracking crops with drones and robots, which is speeding up breeding and conservation efforts but also exposing gaps in data quality and tech access.
Abstract Preview
Original paper
Digital dimension of plant science research: A bird's eye view of AI integration.
Plant science is undergoing a transformation through the integration of artificial intelligence (AI), high-throughput phenotyping, plant biotechnology and multi-omics, and smart digital technologie...
open_in_new Read full abstractAbstract copyright held by the original publisher.
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