利用无标签光谱工具对柑橘叶片病害进行光学筛选:综述

Saurav Bharadwaj, Akshita Midha, Shikha Sharma, Gurupkar Singh Sidhu, Rajesh Kumar
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引用次数: 0

摘要

柑橘病害对柑橘种植业构成威胁,并在全球范围内造成经济损失。通常使用基于核酸和血清学的检测方法以及免疫层析检测法,但这些实验室检测方法费时费力、成本高昂,而且可能会产生交叉反应和污染。现代光学光谱技术提供了一种很有前景的替代方法,因为它们无需标记、灵敏、快速、无破坏性,并证明了将其纳入柑橘园疾病检测自主系统的潜力。尽管如此,用于柑橘病害检测的大多数光学光谱方法仍处于试验阶段,需要进一步努力才能成为高效且商业上可行的方法。本综述概述了光学光谱技术的基本工作原理和技术现状,并解释了光学光谱技术(包括光谱成像方法(高光谱成像))在识别柑橘植物病害方面的应用和局限性。综述重点介绍了:(1) 有可能用于实地测量的光学光谱工具的技术规格;(2) 它们在通过叶片光谱筛选柑橘病害方面的应用;(3) 讨论了它们的优点和局限性,包括对柑橘病害无标记鉴定的未来展望。此外,文章还评述了人工智能作为光谱分析潜在有效工具的作用,通过利用植物叶片的构成、形态和化学计量特征,甚至在出现视觉症状之前就能更准确地检测出受感染的柑橘叶片。该综述旨在鼓励利益相关者加强基于现场的无标记光学工具的开发和商业化,以便对柑橘植物病害进行快速和早期筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical Screening of Citrus Leaf Diseases Using Label-Free Spectroscopic Tools: A Review
Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.
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