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新型植物病害检测技术综述。

Novel plant disease detection techniques-a brief review.

机构信息

National Institute of Technology, Warangal, Telangana, India.

出版信息

Mol Biol Rep. 2023 Nov;50(11):9677-9690. doi: 10.1007/s11033-023-08838-y. Epub 2023 Oct 12.

Abstract

Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.

摘要

植物病原体在全球范围内对农业产量造成严重损失。跟踪植物健康状况和早期疾病检测对于减少疾病传播和经济损失非常重要。尽管从过去开始就已经进行了目视侦察,但无症状疾病情况的检测很困难。因此,基于 DNA 和血清学的方法在植物病害检测中变得重要。先进技术的进步挑战了快速、非侵入性和现场检测技术的发展,如光谱学。本综述强调了各种直接和间接的方法来检测植物病害,如酶联免疫吸附测定、侧向流动分析、聚合酶链反应、光谱技术和生物传感器。尽管这些技术具有敏感性和病原体特异性,但它们更繁琐且耗时。因此,人们对具有人工智能辅助早期病原体检测功能的现场适应性即时检测设备产生了浓厚的兴趣。最近,神经网络等计算机辅助技术在图像处理方面在植物病害检测中也得到了重视。此外,还提供了关于植物病害检测技术最新进展的简明报告。

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