|
AI-Powered Plant Biotechnology: Revolutionizing Gene Editing, Stress Tolerance, and Predictive Breeding
|
Mojtaba Kordrostami   , Ali Akbar Ghasemi-Soloklui , Saeed Moori , Mehdi Rahimi |
| Department of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran |
|
|
Abstract: (107 Views) |
| Artificial intelligence (AI) has rapidly emerged as a transformative force in plant biotechnology, reshaping a wide range of applications, from multi-omics data analysis to genome design, predictive breeding, and smart agriculture. The explosive growth of biological data has created an urgent need for models capable of uncovering hidden patterns, deciphering nonlinear relationships, and accurately predicting plant behavior under diverse environmental conditions. This review synthesizes recent advances in machine learning and deep learning for genomic, transcriptomic, proteomic, and metabolomic analysis. It highlights the expanding role of AI in enhancing the precision and efficiency of gene-editing systems, particularly CRISPR-based technologies. We further discuss breakthroughs in digital phenotyping, machine vision, and automated detection of biotic and abiotic stresses. Key developments in predictive breeding, multi-modal genotype–environment modeling, plant Digital Twins, and AI-driven systems biology—including the modeling of gene regulatory and metabolic networks—are examined in depth. Alongside these opportunities, challenges such as data quality issues, model interpretability, dataset bias, ethical considerations, and biosecurity concerns are critically evaluated. Finally, we outline future directions featuring computational genome design, robotic agriculture, autonomous breeding workflows, and fully integrated AI-powered plant management systems. Collectively, this review demonstrates how the convergence of AI and plant biotechnology is paving the way for the development of resilient, high-yielding, and climate-adaptive crops tailored to the demands of future agriculture. |
|
| Keywords: Artificial Intelligence, Plant Biotechnology, CRISPR Gene Editing, Digital Phenotyping, Predictive Breeding |
|
|
Full-Text [PDF 1213 kb]
(54 Downloads)
|
Type of Study: Research |
Subject:
Special
Received: 2025/12/4 | Accepted: 2025/10/2 | Published: 2025/10/2
|
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
