REVIEW article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
AI-Driven Advances in Plant Biotechnology: Sharpening the Edge of Plant Tissue Culture and Genome Editing
Provisionally accepted
- 1National Research Council Canada Saskatoon, Saskatoon, Canada
- 2Anandi Botanicals Inc., Lethbridge, Alberta, Canada
- 3National Research Council Canada (NRC), Ottawa, Canada
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The advent of artificial intelligence (AI) holds great promise for revolutionizing the fields of plant tissue culture and genome editing. Plant tissue culture is recognized as a powerful tool for rapid multiplication and crop improvement. However, the complex interactions between genetic and environmental factors generate large volumes of data, posing challenges for traditional statistical analysis methods. To address this, researchers are now employing machine learning (ML)-based and artificial neural networks (ANN) approaches to predict and optimize in vitro culture protocols thereby improving precision, sustainability, and efficiency. Integrating AI technologies such as machine learning (ML), artificial neural networks (ANN), and deep learning (DL) can significantly advance the development of data-driven models for CRISPR/Cas9 genome editing. Today, AI-driven methods are routinely applied to enhance precision in predicting on-and off-target sequence locations and editing outcomes. Additionally, predicting protein structures can provide a directed evolution framework that facilitates the creation of improved gene editing tools. However, the application of AI-based CRISPR modeling in plants is not yet fully explored. In this context, we aim to examine representative ML/DL/ANN models of CRISPR/Cas based editing employed in various organisms. This review significantly compiles a diverse set of studies and provides a clear overview of how AI is transforming the fields of plant tissue culture and genome editing. It emphasizes AI's potential to increase the efficiency and precision of biotechnological practices, making them more accessible and cost-effective. While outlining current findings, the paper sets the stage for future research, encouraging further exploration into the integration of AI with plant biotechnology.
Keywords: artificial intelligence, plant tissue culture, Genome editing, machine learning, deep learning, CRISPR
Received: 04 Oct 2025; Accepted: 20 Nov 2025.
Copyright: © 2025 Narra, Ray, Polley, Yang and Bhowmik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Pankaj Kumar Bhowmik, pankaj.bhowmik@nrc-cnrc.gc.ca
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.