用深度学习自动分析植物拉曼光谱，无须预处理即可检测多种胁迫。
Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis
- 基于变分自编码器的全自动流程，直接处理含荧光背景的原始光谱。
- 成功识别并量化多种非生物与生物胁迫下的关键振动特征，准确率高。
- 适合农业科研人员和智能农作系统开发者，推动精准植保应用。
植物胁迫检测对开放农场和受控环境农业至关重要。植物内的生物分子是关键胁迫指标，可作为持续健康监测和早期疾病预警的重要标记。拉曼光谱通过分子振动特征提供了一种强大的无创检测手段来量化这些生物分子。然而，传统拉曼分析依赖于定制化数据处理流程，需手动去除荧光背景并预先识别感兴趣的拉曼峰，易引入偏差与不一致。本文提出DIVA（Deep-learning-based Investigation of Vibrational Raman spectra for plant-stress Analysis），一种基于变分自编码器的全自动化工作流。不同于传统方法，DIVA可直接处理含荧光背景的原始拉曼光谱，无需人工预处理，以无偏方式识别并量化显著的光谱特征。我们将DIVA应用于多种植物胁迫检测，包括非生物胁迫（遮荫、强光、高温）和生物胁迫（细菌感染）。通过将深度学习与振动光谱结合，DIVA为人工智能驱动的植物健康评估开辟了新路径，助力更可持续、更具韧性的农业实践。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。