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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2507.15772 |
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| _version_ | 1866913951324831744 |
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| author | Patil, Anoop C. Sng, Benny Jian Rong Chang, Yu-Wei Pereira, Joana B. Nam-Hai, Chua Sarojam, Rajani Singh, Gajendra Pratap Jang, In-Cheol Volpe, Giovanni |
| author_facet | Patil, Anoop C. Sng, Benny Jian Rong Chang, Yu-Wei Pereira, Joana B. Nam-Hai, Chua Sarojam, Rajani Singh, Gajendra Pratap Jang, In-Cheol Volpe, Giovanni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis Patil, Anoop C. Sng, Benny Jian Rong Chang, Yu-Wei Pereira, Joana B. Nam-Hai, Chua Sarojam, Rajani Singh, Gajendra Pratap Jang, In-Cheol Volpe, Giovanni Machine Learning Artificial Intelligence Biomolecules 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. |
| title | Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis |
| topic | Machine Learning Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2507.15772 |