Non-destructive Classification of Phytophthora Blight and Water Stress in Perilla Using Hyperspectral Imaging and 1D-CNN

Tae-An Kang; Chi-Yong An; Youngnam Yoon; Jongsoo Ryu; Hongseok Lee

doi:10.22698/jales.20260010

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2026 Vol.38, Issue 2 Preview Page Next Page

Research Article

Non-destructive Classification of Phytophthora Blight and Water Stress in Perilla Using Hyperspectral Imaging and 1D-CNN

30 June 2026. pp. 136-150

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Abstract

Phytophthora blight and water stress in perilla (Perilla frutescens L.) exhibit similar foliar symptoms, making visual differentiation challenging. In this study, we developed a non-destructive classification method using hyperspectral imaging (HSI) in conjunction with Partial Least Squares Discriminant Analysis (PLS-DA) and a one-dimensional convolutional neural network (1D-CNN). The “Anyu” perilla cultivar was subjected to four treatments: control, drought, waterlogging, and inoculation with Phytophthora nicotianae MR40622. Hyperspectral images (400－1,000 nm) were acquired daily, and pixel-level spectra were preprocessed using Raw, Savitzky-Golay (SG) smoothing, Standard Normal Variate (SNV), and first-derivative methods. Classification was performed across three stages of stress progression: early, middle, and late. PLS-DA demonstrated limited sensitivity to preprocessing, achieving a maximum F1 score of 0.688 at the late stage when using SNV. Conversely, the 1D-CNN model combined with first-derivative preprocessing consistently yielded the highest performance, reaching F1 scores of 0.527, 0.772, and 0.827, at the early, middle, and late stages, respectively. The performance gap between PLS-DA and 1D-CNN widened from 0.004 to 0.139 as stress increased, indicating that the 1D-CNN effectively captured nonlinear spectral patterns. Saliency map analysis revealed that the chlorophyll absorption band at 660－690 nm contributed most to classification across all stages, while the water absorption band at 900－950 nm exhibited an increased contribution for the drought and waterlogging treatments during the late stage. These results demonstrate that HSI combined with a 1D-CNN serves an effective, non-destructive diagnostic tool for distinguishing between Phytophthora blight and water stress in perilla.

Keywords

1D-CNN

Hyperspectral imaging

Perilla

Phytophthora

Water stress

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Information

Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
Publisher(Ko) :None
Journal Title :Journal of Agricultural, Life and Environmental Sciences
Volume : 38
No :2
Pages :136-150
Received Date : 2026-04-02
Revised Date : 2026-04-03
Accepted Date : 2026-04-13
DOI :https://doi.org/10.22698/jales.20260010

Journal of Agricultural, Life and Environmental Sciences ISSN:2233-8322(Print) 2508-870X(Online)

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