Integrating AI and CRISPR Cas13a for rapid detection of tomato brown rugose fruit virus

The Tomato Brown Rugose Fruit Virus (ToBRFV) has recently emerged as a serious threat to global tomato production, underscoring the need for rapid and sensitive diagnostic tools. Here, we present an AI-driven CRISPR-Cas13a pipeline for designing crRNAs with high specificity to enable the detection of ToBRFV. A computational pipeline that retrieves viral sequences, aligns them in multiple sequence alignments, analyzes their conservation, and screens for off-targets-all coupled with machine learning to optimize crRNA sequences. Experimentally validated crRNAs were evaluated with a fluorescence-based Cas13a assay and showed better sensitivity than RT-PCR, RT-qPCR, and RT-LAMP. By the CRISPR-Cas13a system, ToBRFV was detected at 1:200 (1 ng/µL) dilutions, which performed superior to conventional methods. Integrating bioinformatics with experimental workflows, this pipeline provides a powerful framework for rapid diagnostics that can be deployed in the field, addressing significant challenges in plant virus surveillance and management.