Smartphone-Based Colorimetric VOC Sensor for Early Detection of Phytophthora Ramorum in Rhododendrons

Traditional plant pathogen detection often relies on molecular technologies, which allow species-level detection but are often time-consuming. Plant volatile organic compounds (VOCs) have recently been harnessed to assist in disease detection and plant health monitoring. However, current VOC detection methods are unsuitable for field use due to the need for expensive laboratory equipment and slow processing times. To address this, we developed a portable paper-based colorimetric sensing technology for early detection of ramorum blight in rhododendron caused by Phytophthora ramorum. This colorimetric sensor array, which includes nanomaterials and organic dyes, was optimized to detect alcohol, terpene, and ester, key VOC biomarkers emitted by infected rhododendron leaves. Color quantification was done quickly by smartphone imaging. Principal component analysis (PCA) was used to cluster and classify individual plant volatiles. Our VOC sensing platform detected ramorum blight 2 days after inoculation, aligning with real-time loop-mediated isothermal amplification (LAMP) analysis. Moreover, the platform distinguished pathogen-induced VOCs from those produced by nonbiological stresses such as drought and mechanical damage. This noninvasive diagnostic technology demonstrates significant potential for disease detection in the field.