CRISPR-CAS9 and Artificial Intelligence: Shaping the Future of Precision Crop Improvement

Author

Nikita Hiwale, Komal Birajdar


Abstract

 The increasing demand for sustainable and resilient crop production has accelerated the development of precision breeding technologies. Conventional breeding remains essential, but limited genetic diversity, complex traits and lengthy breeding cycles highlight the need for complementary approaches. CRISPR-based genome editing enables targeted genetic modification, while advances such as base editing, prime editing, promoter editing and multiplex editing have expanded its potential in crop improvement. Artificial intelligence (AI) and machine learning can complement these technologies by analysing genomic, phenotypic and multi-omics datasets, identifying candidate targets, assisting guide RNA design and predicting editing outcomes. This article reviews the emerging integration of AI with CRISPR-based genome editing for precision crop improvement, with applications in climate resilience, disease resistance, nutritional quality, yield-related traits and de novo domestication. A recent rice study using an AI-designed OpenCRISPR-1 nuclease highlights the growing potential of computational approaches in plant genome engineering. The article also discusses current challenges, including data quality, model reliability, biological complexity, delivery, experimental validation and regulatory considerations. Overall, integrating AI with genomics and genome editing could provide a more precise and data-driven framework for crop improvement, provided that computational predictions are supported by rigorous experimental and field validation.



Keywords

Artificial intelligence, base editing, CRISPR-Cas9, genome editing, guide RNA design, machine learning, prime editing.



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References


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