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Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease.

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Title: Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease.
Authors: Zhou, Chen1 (AUTHOR), Bucklew, Victor G.2 (AUTHOR), Edwards, Perry S.2 (AUTHOR), Zhang, Chenji1 (AUTHOR), Yang, Jinkai1 (AUTHOR), Ryan, Philip J.1 (AUTHOR), Hughes, David P.3 (AUTHOR), Qu, Xinshun4 (AUTHOR), Liu, Zhiwen1 (AUTHOR) zzl1@psu.edu
Source: Applied Spectroscopy. May2023, Vol. 77 Issue 5, p491-499. 9p.
Subjects: Reflectance spectroscopy, Late blight of potato, Plant diseases, Optical spectroscopy, Phytophthora infestans, Potato diseases & pests, Potatoes, Blackberries
Abstract: We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen Phytophthora infestans. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen Phytophthora infestans. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]
ISSN:00037028
DOI:10.1177/00037028231165342