References

vavilov

Вавиловский журнал генетики и селекции

Vavilov Journal of Genetics and Breeding

2500-3259

Institute of Cytology and Genetics of Siberian Branch of the RAS

10.18699/VJGB-22-25

vavilov-3297

Research Article

АКТУАЛЬНЫЕ ТЕХНОЛОГИИ ГЕНЕТИКИ РАСТЕНИЙ

MAINSTREAM TECHNOLOGIES IN PLANT GENETICS

Обзор современных методов обнаружения и идентификации болезней растений на основе анализа гиперспектральных изображений

A review of hyperspectral image analysis techniques for plant disease detection and identif ication

https://orcid.org/0000-0003-2265-7129

Чешкова

А. Ф.

Cheshkova

A. F.

р.п. Краснообск, Новосибирская область

Krasnoobsk, Novosibirsk region

cheshanna@yandex.ru

Сибирский федеральный научный центр агробиотехнологий Российской академии наукРоссияSiberian Federal Scientif ic Center of AgroBioTechnology of the Russian Academy of SciencesRussian Federation

2022

05042022

262202213

2022

Чешкова А.Ф.

Cheshkova A.F.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://vavilov.elpub.ru/jour/article/view/3297

Болезни растений приводят к значительным экономическим потерям в секторе сельскохозяйственного производства во всем мире. Раннее выявление, количественная оценка и идентификация болезней имеют решающее значение для целенаправленного применения мер защиты в растениеводстве. В настоящее время ведутся интенсивные научные исследования по разработке инновационных методов диагностики болезней растений, основанных на гиперспектральных технологиях. Анализ спектра отражения растительной ткани позволяет проводить классификацию здоровых и больных растений, оценивать тяжесть заболевания, дифференцировать виды патогенов и выявлять симптомы биотических стрессов на ранних стадиях, в том числе в инкубационный период, когда симптомы не видны человеческому глазу. В обзоре описаны основные принципы измерения спектра отражения растительной ткани. Обсуждаются и оцениваются возможности применения различных типов гиперспектральных сенсоров и платформ для диагностики болезней растений. Гиперспектральный анализ является новой областью, соединяющей в себе методы оптической спектроскопии и методы анализа изображений, которые позволяют одновременно оценивать как физиологические, так и морфологические параметры. Описаны главные этапы анализа гиперспектральных данных: получение и предварительная обработка изображения; извлечение и обработка данных; моделирование и анализ данных. Приведен перечень алгоритмов и методов, применяемых на каждом из этапов. Рассмотрены основные области применения гиперспектральных сенсоров в диагностике болезней растений, такие как обнаружение болезни, дифференциация и идентификация типа заболевания, оценка степени поражения, оценка устойчивости генотипов. Приведен всесторонний обзор научных публикаций, подчеркивающий преимущества гиперспектральных технологий при исследовании взаимодействий между растениями и патогенами в различных масштабах измерений. Несмотря на обнадеживающий прогресс, достигнутый за последние несколько десятилетий в мониторинге болезней растений на основе гиперспектральных технологий, остаются нерешенными некоторые технические проблемы, препятствующие применению этих методов на практике. В заключение обсуждаются проблемы и перспективы практического использования новых технологий в сельскохозяйственном производстве.

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 preprocessing; 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.

гиперспектральные технологииболезни растенийанализ изображенийспектральный анализ

hyperspectral technologiesplant diseasesimage analysisspectral analysis

This work was supported by the Russian Science Foundation, project No. 0533-2021-0007.

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The authors declare that there are no conflicts of interest present.