Journal of Advances in Biology & Biotechnology

Microfluidic technology is revolutionizing plant disease diagnostics by offering rapid, precise, and cost-effective detection tools compatible with field-based deployment. As plant diseases continue to threaten global food security and agricultural sustainability, the need for advanced diagnostic platforms that overcome the limitations of traditional methods such as culture-based, serological, and PCR assays has become critical. Microfluidic systems enable miniaturized sample handling, on-chip reagent mixing, nucleic acid amplification, and multiplexed detection of bacterial, fungal, viral, and nematode pathogens with reduced reagent use and diagnostic time. These devices are increasingly integrated with isothermal amplification methods like LAMP and RPA, biosensors, and smartphone-based readout systems for real-time visualization and geo-referenced data collection. Innovations in paper-based and thermoplastic microchips, CRISPR-Cas-based biosensing, nanomaterial-enhanced signal amplification, and portable energy sources are enhancing the accessibility and performance of microfluidic diagnostics. Despite these advantages, technical bottlenecks such as device clogging, matrix interference from plant tissues, and a lack of standardized validation protocols pose challenges to large-scale adoption. Efforts to address these issues include the development of single-cell microfluidics for host-pathogen interaction studies, high-throughput multiplex platforms, and open-source, modular designs suitable for customization. When coupled with IoT-enabled surveillance networks, cloud-based data systems, and AI-driven decision support, microfluidic tools enable spatial disease modelling, early outbreak forecasting, and site-specific disease management. These capabilities align with the principles of precision agriculture by optimizing agrochemical application, reducing input costs, and minimizing environmental impact. Realizing the full potential of microfluidics in plant health requires interdisciplinary collaboration among plant pathologists, engineers, biochemists, and data scientists. Such integration will foster the development of scalable, validated, and policy-compliant diagnostic systems capable of transforming agricultural disease management.