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Plant Biospectroscopy for Stress Detection

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 2781

Special Issue Editors


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Guest Editor
Faculty of Sciences, Hasselt University, 3590 Diepenbeek, Belgium
Interests: photosynthesis; remote sensing (hyperspectral, fluorescence); imaging; stress physiology; photovoltaics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Pesquisador nas Áreas de Processamento Digital de Sinais e Imagens, Embrapa Informática Agropecuária, Empresa Brasileira de Pesquisa Agropecuária (Embrapa), Campinas/SP, Brazil
Interests: digital signal processing; digital audio processing; digital image processing; computer vision

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Guest Editor
Departmento de Quínica Inorgánica, Analítica y Química Física, Faculdad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Interests: fluorescence; spectral analysis; fluorescence imaging; photophysics

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Guest Editor
Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
Interests: GIS and remote sensing; unmanned aerial systems (UAV); earth observation; vegetation; ecology; environment; cryosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The performance of plants in their natural, uncultivated habitats and agricultural environments is strongly under the control of abiotic and biotic stresses. The process driving the growth and development of all plants is photosynthesis. During this process, absorbed light energy will be converted into chemical energy (ATP and NADPH) by the cooperation of pigment–protein complexes in the so-called light reactions. This energy is then the driving force to reduce carbon dioxide into sugars in the Calvin–Benson–Bassham cycle. From here on, other organic components necessary for biomass production will be synthesized. However, part of the absorbed light will be reemitted as fluorescence.

Leaves are characterized by the spatial heterogeneity of the photosynthetic performance, which reflects metabolic differences in different cells. These may be caused by internal factors such as variations in cell/leaf physiology during development or by external abiotic/biotic stresses. Visual manifestations of stress are well documented but cannot discriminate enough between similar symptoms induced by different stresses. Additionally, they do not give sufficient information on the underlying physiological processes. Powerful, non-invasive tools to resolve the spatial heterogeneity are spectral reflectance spectroscopy at well-defined wavelengths in the visible and infra-red range and blue/green and red/far-red fluorescence imaging.

This Special Issue will focus on the exploitation of the UV-induced blue-green fluorescence, the passive/active induced chlorophyll fluorescence and spectroscopic imaging to analyze plant stresses.

To retrieve robust and reliable quantitative information from the images and couple this with physiological and structural parameters, research papers combining these disciplines will be welcome.

Original research papers and reviews about the following topics in relation to plant abiotic/biotic stress (abiotic stress: drought/water, temperature, mineral/nutrient, air pollution, mechanical, etc.; biotic stress: viral, bacterial and fungal infections/diseases, insect infestations, etc.):

  • Spectroscopic methods;
  • Multi/Hyperspectral spectroscopy and imaging;
  • Proximal and remote (active/passive) multicolour fluorescence imaging methods;
  • Fluorescence induction and kinetics—modulated fluorescence;
  • Multispectral fluorescence imaging systems for plant leaves;
  • Systems for imaging variable chlorophyll fluorescence;
  • Sequential Excitation/Emission Imaging;
  • Laser-induced plant fluorescence lifetime imaging;
  • Sun-induced fluorescence;
  • Retrieval of fluorescence from solar Fraunhofer lines;
  • Quantification of non-photochemical quenching mechanisms;
  • Image processing—image segmentation;
  • Statistical and classification analysis methods;
  • Neural network and machine learning protocols/algorithms—phenotyping;
  • Physiological and structural analysis methods.

Dr. Roland Valcke
Dr. Jayme Garcia Arnal Barbedo
Dr. Maria Gabriela Lagorio
Dr. Micol Rossini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Nutrient/mineral stress in ecosystems and crops
  • Fluorescence imaging of vegetation
  • Image analysis
  • Non-photochemical quenching
  • Phenotyping
  • Applications in ecology and agriculture

Published Papers (1 paper)

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Research

21 pages, 5776 KiB  
Article
Assessing Nitrogen Variability at Early Stages of Maize Using Mobile Fluorescence Sensing
by Rafael Siqueira, Dipankar Mandal, Louis Longchamps and Raj Khosla
Remote Sens. 2022, 14(20), 5077; https://doi.org/10.3390/rs14205077 - 11 Oct 2022
Cited by 2 | Viewed by 2060
Abstract
Characterizing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Practitioners [...] Read more.
Characterizing nutrient variability has been the focus of precision agriculture research for decades. Previous research has indicated that in situ fluorescence sensor measurements can be used as a proxy for nitrogen (N) status in plants in greenhouse conditions employing static sensor measurements. Practitioners of precision N management require determination of in-season plant N status in real-time in the field to enable the most efficient N fertilizer management system. The objective of this study was to assess if mobile in-field fluorescence sensor measurements can accurately quantify the variability of nitrogen indicators in maize canopy early in the crop growing season. A Multiplex®3 fluorescence sensor was used to collect crop canopy data at the V6 and V9 maize growth stages. Multiplex fluorescence indices were successful in discriminating variability among N treatments with moderate accuracies at V6, and higher at the V9 stage. Fluorescence-based indices were further utilized with a machine learning (ML) model to estimate canopy nitrogen indicators i.e., N concentration and above-ground biomass at the V6 and V9 growth stages independently. Parameter estimation using the Support Vector Regression (SVR)-based ML mode indicated a promising accuracy in estimation of N concentration and above-ground biomass at the V6 stage of maize with the moderate range of correlation coefficient (r = 0.72 ± 0.03) and Root Mean Square Error (RMSE). The retrieval accuracies (r = 0.90 ± 0.06) at the V9 stage were better than those of the V6 growth stage with a reasonable range of error estimates and yielding the lowest RMSE (0.23 (%N) and 12.37 g (biomass)) for all canopy N indicators. Mobile fluorescence sensing can be used with reasonable accuracies for determining canopy N variability at early growth stages of maize, which would help farmers in optimal management of nitrogen. Full article
(This article belongs to the Special Issue Plant Biospectroscopy for Stress Detection)
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