Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles

Abstract

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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Fig. 1: Design of the smartphone imaging platform for plant volatile sensing.
Fig. 2: Vapour detection of the characteristic C6 plant aldehyde using functionalized Au NRs.
Fig. 3: Sensor response of the multiplex array to ten major plant volatiles at the vapour level of 10 ppm for 1 min exposure and their chemometric analysis.
Fig. 4: Detection of P. infestans in tomato leaves by the smartphone VOC-sensor.
Fig. 5: Validation of the specificity of the smartphone-based VOC-sensor.
Fig. 6: Evaluation of the smartphone-based VOC-sensor for blind detection of P. infestans in tomato leaves.

Data availability

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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Acknowledgements

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.

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Z.L., Q.W. and J.B.R. designed the experiments. Z.L. prepared the Au nanoplasmonic inks and the multiplexed chemical sensor array, carried out sensing experiments and analysed data. R.P. conducted the COMSOL simulation of gas flows. J.C.H. collected the fresh field samples for qPCR and VOC tests. R.P., T.Y., Z.L., A.C.S. and J.C.H. performed the leaf inoculation experiments and conducted PCR or qPCR analyses of the pathogens. Z.L. and T.B.T. designed and fabricated the smartphone imaging attachment. Z.L., Q.W. and J.B.R. wrote the manuscript. All authors contributed to the editing of the manuscript.

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Correspondence to Qingshan Wei.

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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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