Conventional and cutting-edge advances in plant virus detection: emerging trends and techniques

Plant viruses pose a significant threat to global agriculture. For a long time, conventional methods including detection based on visual symptoms, host range investigations, electron microscopy, serological assays (e.g., ELISA, Western blotting), and nucleic acid-based techniques (PCR, RT-PCR) have been used for virus identification. With increased sensitivity, speed, and specificity, new technologies like loop-mediated isothermal amplification (LAMP), high-throughput sequencing (HTS), nanotechnology-based biosensors, and CRISPR diagnostics have completely changed the way plant viruses are detected. Recent advances in detection techniques integrate artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) for real-time monitoring. Innovations like hyperspectral imaging, deep learning, and cloud-based IoT platforms further support disease identification and surveillance. Nanotechnology-based lateral flow assays and CRISPR-Cas systems provide rapid, field-deployable solutions. Despite these advancements, challenges such as sequence limitations, multiplexing constraints, and environmental concerns remain. Future research should focus on refining portable on-site diagnostic kits, optimizing nanotechnology applications, and enhancing global surveillance systems. Interdisciplinary collaboration across molecular biology, bioinformatics, and engineering is essential to developing scalable, cost-effective solutions for plant virus detection, ensuring agricultural sustainability and ecosystem protection.