| Title: | Non-invasive plant disease diagnostics enabled by smartphone-based fingerprinting of leaf volatiles |
| Author(s): | Li Z; Paul R; Ba Tis T; Saville AC; Hansel JC; Yu T; Ristaino JB; Wei Q; |
| Address: | "Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA. Department of Materials Science and Engineering, North Carolina State University, Raleigh, NC, USA. Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA. Emerging Plant Disease and Global Food Security Cluster, North Carolina State University, Raleigh, NC, USA. Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA. qwei3@ncsu.edu. Emerging Plant Disease and Global Food Security Cluster, North Carolina State University, Raleigh, NC, USA. qwei3@ncsu.edu" |
| DOI: | 10.1038/s41477-019-0476-y |
| ISSN/ISBN: | 2055-0278 (Electronic) 2055-0278 (Linking) |
| 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" |
| Keywords: | *Mobile Applications Phytophthora infestans/physiology *Plant Diseases/microbiology Plant Leaves/chemistry *Smartphone Solanum tuberosum/*microbiology Volatile Organic Compounds/analysis; |
| Notes: | "MedlineLi, Zheng Paul, Rajesh Ba Tis, Taleb Saville, Amanda C Hansel, Jeana C Yu, Tao Ristaino, Jean B Wei, Qingshan eng Evaluation Study Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. England 2019/07/31 Nat Plants. 2019 Aug; 5(8):856-866. doi: 10.1038/s41477-019-0476-y. Epub 2019 Jul 29" |
