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arXiv · 2507.15772

Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis

Anoop C. Patil·Benny Jian Rong Sng·Yu-Wei Chang·Joana B. Pereira·Chua Nam-Hai·Rajani Sarojam·Gajendra Pratap Singh·In-Cheol Jang·Giovanni Volpe
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Abstract

Detecting stress in plants is crucial for both open-farm and controlled-environment agriculture. Biomolecules within plants serve as key stress indicators, offering vital markers for continuous health monitoring and early disease detection. Raman spectroscopy provides a powerful, non-invasive means to quantify these biomolecules through their molecular vibrational signatures. However, traditional Raman analysis relies on customized data-processing workflows that require fluorescence background removal and prior identification of Raman peaks of interest-introducing potential biases and inconsistencies. Here, we introduce DIVA (Deep-learning-based Investigation of Vibrational Raman spectra for plant-stress Analysis), a fully automated workflow based on a variational autoencoder. Unlike conventional approaches, DIVA processes native Raman spectra-including fluorescence backgrounds-without manual preprocessing, identifying and quantifying significant spectral features in an unbiased manner. We applied DIVA to detect a range of plant stresses, including abiotic (shading, high light intensity, high temperature) and biotic stressors (bacterial infections). By integrating deep learning with vibrational spectroscopy, DIVA paves the way for AI-driven plant health assessment, fostering more resilient and sustainable agricultural practices.

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Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, Giovanni Volpe. 2025-07-21. Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis. https://arxiv.org/abs/2507.15772

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