Literature DB >> 32480543

Observation of plant-pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements.

Stefan Thomas1, Mirwaes Wahabzada1, Matheus Thomas Kuska1, Uwe Rascher2, Anne-Katrin Mahlein1.   

Abstract

Hyperspectral imaging sensors are valuable tools for plant disease detection and plant phenotyping. Reflectance properties are influenced by plant pathogens and resistance responses, but changes of transmission characteristics of plants are less described. In this study we used simultaneously recorded reflectance and transmittance imaging data of resistant and susceptible barley genotypes that were inoculated with Blumeria graminis f. sp. hordei to evaluate the added value of imaging transmission, reflection and absorption for characterisation of disease development. These datasets were statistically analysed using principal component analysis, and compared with visual and molecular disease estimation. Reflection measurement performed significantly better for early detection of powdery mildew infection, colonies could be detected 2 days before symptoms became visible in RGB images. Transmission data could be used to detect powdery mildew 2 days after symptoms becoming visible in reflection based RGB images. Additionally distinct transmission changes occurred at 580-650nm for pixels containing disease symptoms. It could be shown that the additional information of the transmission data allows for a clearer spatial differentiation and localisation between powdery mildew symptoms and necrotic tissue on the leaf then purely reflectance based data. Thus the information of both measurement approaches are complementary: reflectance based measurements facilitate an early detection, and transmission measurements provide additional information to better understand and quantify the complex spatio-temporal dynamics of plant-pathogen interactions.

Entities:  

Year:  2016        PMID: 32480543     DOI: 10.1071/FP16127

Source DB:  PubMed          Journal:  Funct Plant Biol        ISSN: 1445-4416            Impact factor:   3.101


  4 in total

1.  Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review.

Authors:  Katja Berger; Miriam Machwitz; Marlena Kycko; Shawn C Kefauver; Shari Van Wittenberghe; Max Gerhards; Jochem Verrelst; Clement Atzberger; Christiaan van der Tol; Alexander Damm; Uwe Rascher; Ittai Herrmann; Veronica Sobejano Paz; Sven Fahrner; Roland Pieruschka; Egor Prikaziuk; Ma Luisa Buchaillot; Andrej Halabuk; Marco Celesti; Gerbrand Koren; Esra Tunc Gormus; Micol Rossini; Michael Foerster; Bastian Siegmann; Asmaa Abdelbaki; Giulia Tagliabue; Tobias Hank; Roshanak Darvishzadeh; Helge Aasen; Monica Garcia; Isabel Pôças; Subhajit Bandopadhyay; Mauro Sulis; Enrico Tomelleri; Offer Rozenstein; Lachezar Filchev; Gheorghe Stancile; Martin Schlerf
Journal:  Remote Sens Environ       Date:  2022-08-04       Impact factor: 13.850

Review 2.  Research Progress on the Early Monitoring of Pine Wilt Disease Using Hyperspectral Techniques.

Authors:  Weibin Wu; Zhenbang Zhang; Lijun Zheng; Chongyang Han; Xiaoming Wang; Jian Xu; Xinrong Wang
Journal:  Sensors (Basel)       Date:  2020-07-03       Impact factor: 3.576

Review 3.  Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography.

Authors:  Angelica Galieni; Nicola D'Ascenzo; Fabio Stagnari; Giancarlo Pagnani; Qingguo Xie; Michele Pisante
Journal:  Front Plant Sci       Date:  2021-01-27       Impact factor: 5.753

4.  Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning.

Authors:  Canh Nguyen; Vasit Sagan; Matthew Maimaitiyiming; Maitiniyazi Maimaitijiang; Sourav Bhadra; Misha T Kwasniewski
Journal:  Sensors (Basel)       Date:  2021-01-22       Impact factor: 3.576

  4 in total

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