Title: Raman Spectroscopy for the Early Detection of Huanglongbing and Citrus Canker in Plants: A Review Show Chinese title

Title: 拉曼光谱在植物黄龙病和柑橘溃疡病早期检测中的应用：综述

Abstract: Citrus crops are of immense economic and agricultural importance worldwide, but are highly vulnerable to destructive diseases such as Huanglongbing (citrus greening) and citrus canker. These diseases often remain asymptomatic in early stages, making timely diagnosis difficult and resulting in substantial yield and quality losses. Rapid, non-invasive, and accurate early detection methods are therefore essential for effective disease management and sustainable citrus production. Raman spectroscopy, with its molecular specificity, non-destructive nature, and minimal sample preparation requirements, has emerged as a promising diagnostic technology for this purpose. This review outlines recent advances in applying Raman spectroscopy for the early detection of Huanglongbing and citrus canker, with a focus on spectral biomarkers that reflect pathogen-induced physiological and biochemical alterations in citrus tissues. Comparative insights are provided between portable, in-field Raman devices and conventional laboratory-based systems, highlighting diagnostic accuracy, operational feasibility, and deployment potential. The integration of chemometric and machine learning techniques for enhanced classification and automated disease recognition is examined. By consolidating current research, this review underscores the potential of Raman spectroscopy as a field-deployable solution for precision agriculture. It discusses future challenges and opportunities for the large-scale adoption of this technology in citrus orchard monitoring. Show Chinese abstract

Abstract: 柑橘作物在全球范围内具有巨大的经济和农业重要性，但极易受到诸如黄龙病（柑橘衰退病）和柑橘溃疡病等破坏性疾病的侵害。这些疾病在早期阶段通常没有症状，使得及时诊断变得困难，并导致显著的产量和质量损失。因此，快速、无创和准确的早期检测方法对于有效的疾病管理和可持续的柑橘生产至关重要。拉曼光谱以其分子特异性、无损性和最小的样品制备要求，已成为一种有前景的诊断技术。本综述概述了近年来将拉曼光谱应用于黄龙病和柑橘溃疡病早期检测的进展，重点介绍了反映病原体引起的柑橘组织生理和生化变化的光谱生物标志物。本文提供了便携式现场拉曼设备与传统实验室系统之间的比较见解，突出了诊断准确性、操作可行性和部署潜力。探讨了化学计量学和机器学习技术的整合以提高分类和自动疾病识别。通过综合现有研究，本综述强调了拉曼光谱作为田间可部署解决方案在精准农业中的潜力。它讨论了该技术在柑橘果园监测中大规模采用的未来挑战和机遇。