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Artificial intelligence in plant biotechnology: Advances in gene editing, stress tolerance, and predictive breeding
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M. Kordrostami   , A.A. Ghasemi-Soloklui , S. Moori , M. Rahimi * |
| Department of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran. |
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Abstract: (6 Views) |
Artificial intelligence (AI) has become a powerful tool in plant biotechnology, transforming applications ranging from multi-omics data analysis to genome engineering, predictive breeding, and precision agriculture. The rapid expansion of biological datasets has created an increasing demand for computational models capable of identifying complex patterns, capturing nonlinear relationships, and predicting plant responses under diverse environmental conditions. This review synthesizes recent advances in machine learning and deep learning approaches for genomic, transcriptomic, proteomic, and metabolomic analyses, with particular emphasis on their integration into plant biotechnology. It highlights the growing role of AI in improving the precision and efficiency of gene-editing technologies, particularly CRISPR-based systems, and examines recent developments in digital phenotyping, machine vision, and automated detection of biotic and abiotic stresses. The review further explores AI-driven predictive breeding, multimodal genotype–environment modeling, plant digital twins, and systems biology approaches for modeling gene regulatory and metabolic networks. In addition, major challenges—including data quality, model interpretability, dataset bias, ethical considerations, and biosecurity issues—are critically discussed. Finally, future perspectives on computational genome design, robotic agriculture, autonomous breeding pipelines, and integrated AI-enabled crop management systems are presented. Overall, the convergence of AI and plant biotechnology offers significant opportunities to accelerate the development of resilient, high-yielding, and climate-adaptive crop varieties for sustainable agriculture.
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| Keywords: Artificial intelligence, Plant biotechnology, CRISPR gene editing, Digital phenotyping, Predictive breeding |
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Full-Text [PDF 745 kb]
(2 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2026/06/21 | Accepted: 2026/10/2 | Published: 2026/10/2
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