Cheshkova A F
Siberian Federal Scientif ic Center of AgroBioTechnology of the Russian Academy of Sciences, Krasnoobsk, Novosibirsk region, Russia.
Vavilovskii Zhurnal Genet Selektsii. 2022 Mar;26(2):202-213. doi: 10.18699/VJGB-22-25.
Plant diseases cause signif icant economic losses in agriculture around the world. Early detection, quantif ication and identif ication of plant diseases are crucial for targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. The analysis of the ref lection spectrum of plant tissue makes it possible to classify healthy and diseased plants, assess the severity of the disease, differentiate the types of pathogens, and identify the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors and platforms on different scales for diseases diagnosis are discussed and evaluated. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which make it possible to simultaneously evaluate both physiological and morphological parameters. The review describes the main steps of the hyperspectral data analysis process: image acquisition and prepro cessing; data extraction and processing; modeling and analysis of data. The algorithms and methods applied at each step are mainly summarized. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation and identif ication of diseases, estimation of disease severity, phenotyping of disease resistance of genotypes. A comprehensive review of scientif ic publications on the diagnosis of plant diseases highlights the benef its of hyperspectral technologies in investigating interactions between plants and pathogens at various measurement scales. Despite the encouraging progress made over the past few decades in monitoring plant diseases based on hyperspectral technologies, some technical problems that make these methods diff icult to apply in practice remain unresolved. The review is concluded with an overview of problems and prospects of using new technologies in agricultural production.
植物病害在全球农业中造成了巨大的经济损失。植物病害的早期检测、量化和识别对于在作物生产中有针对性地应用植物保护措施至关重要。最近，人们开展了深入研究，以开发基于高光谱技术诊断植物病害的创新方法。对植物组织反射光谱的分析使得对健康植物和患病植物进行分类、评估病害严重程度、区分病原体类型以及在早期阶段（包括潜伏期，此时肉眼无法看到症状）识别生物胁迫症状成为可能。本综述描述了高光谱测量的基本原理以及不同类型的可用高光谱传感器。讨论并评估了高光谱传感器和平台在不同尺度上用于病害诊断的可能应用。高光谱分析是一个结合了光谱学和图像分析方法的新课题，这使得同时评估生理和形态参数成为可能。本综述描述了高光谱数据分析过程的主要步骤：图像采集与预处理；数据提取与处理；数据建模与分析。主要总结了每个步骤所应用的算法和方法。此外，还考虑了高光谱传感器在植物病害诊断中的主要应用领域，如病害的检测、区分和识别、病害严重程度的估计、基因型抗病性的表型分析。对关于植物病害诊断的科学出版物的全面综述突出了高光谱技术在不同测量尺度上研究植物与病原体之间相互作用的益处。尽管在过去几十年中基于高光谱技术监测植物病害取得了令人鼓舞的进展，但一些使这些方法难以在实践中应用的技术问题仍未得到解决。综述最后概述了在农业生产中使用新技术存在的问题和前景。
