Plant pathogen detection conventionally relies on molecular technology that is complicated, time-consuming and constrained to centralized laboratories. We developed a cost-effective smartphone-based volatile organic compound (VOC) fingerprinting platform that allows non-invasive diagnosis of late blight caused by Phytophthora infestans by monitoring characteristic leaf volatile emissions in the field. This handheld device integrates a disposable colourimetric sensor array consisting of plasmonic nanocolorants and chemo-responsive organic dyes to detect key plant volatiles at the ppm level within 1 min of reaction. We demonstrate the multiplexed detection and classification of ten individual plant volatiles with this field-portable VOC-sensing platform, which allows for early detection of tomato late blight 2 d after inoculation, and differentiation from other pathogens of tomato that lead to similar symptoms on tomato foliage. Furthermore, we demonstrate a detection accuracy of ≥95% in diagnosis of P. infestans in both laboratory-inoculated and field-collected tomato leaves in blind pilot tests. Finally, the sensor platform has been beta-tested for detection of P. infestans in symptomless tomato plants in the greenhouse setting.
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Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis
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The data supporting the findings of this study are available in the paper and its Supplementary Information. All data generated or analysed are available from the corresponding authors on reasonable request.
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This work was supported by the Chancellor’s Faculty Excellence Program, Kenan Institute for Engineering, Technology & Science and USDA AFRI grant (no. 2019-67030-29311). The authors also acknowledge the Analytical Instrumentation Facility at North Carolina State University for assistance in TEM characterization.
The authors declare no competing interests.
Peer review information: Nature Plants thanks Alexander Aksenov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Li, Z., Paul, R., Ba Tis, T. et al. Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles. Nat. Plants 5, 856–866 (2019). https://doi.org/10.1038/s41477-019-0476-y
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