Special Issue "Spectroscopic Analysis of Plants and Vegetation"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Thomas Alexandridis
Website
Guest Editor
Laboratory of Remote Sensing, Spectroscopy and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: agronomic applications of Earth Observation; remote sensing; digital image processing; geoinformatics; spectroscopy; precision agriculture; UAV; crops; irrigation; soil
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Prof. Dr. Dimitrios Moshou
Website
Guest Editor
Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), P.O. 275, 54124 Thessaloniki, Greece
Interests: sensor systems for automated detection and mapping of crop enemies and threat situations (weeds, fungi, viruses, and insects); sensor systems for the detection, recognition, and mapping of nutrient stresses in crops; hyperspectral, multispectral, fluorescence, fluorescence kinetics, computer vision, thermal, lidar, and multisensor systems for crop status sensing and phenotyping; yield mapping in orchards and arable crops by using new technologies (GNSS, RTK-GPS, Zigbee, ambient computing); sensors for viticulture and wine quality; produce and activity traceability systems in the field by using new technologies (RFID, barcode, GPS, Zigbee, wearable computers, etc); bio-inspired information processing, neuroscience, self-organization, and computational intelligence; intelligent control of mechatronic systems; cyber-physical systems; industry 4.0; Internet of Things, and M2M systems; information and data fusion; cognitive robotics and active learning systems, sensor-based environment awareness
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Recent technological advances in sensor and platform technology have led towards the penetration of spectroscopy into new fields of application. In agricultural production, spectroscopy is an emerging field that proves novel applications every day. Spectrometers of higher spectral accuracy and light enough to be carried by commercial UAVs are being used to detect subtle changes in reflectance of plant parts or vegetation canopy. Novel data analysis techniques are being introduced to improve the accuracy and efficiency of the collected spectra, moving towards operational real-time applications.

This special issue aims to bring together recent research and developments concerning spectroscopic analysis of plants and vegetation. Submissions on the following topics are invited (but not limited to), as long as they present innovative methods and approaches, or novel applications of existing tools on spectroscopy of plants and vegetation:

  • Point spectroscopy
  • Imaging spectroscopy
  • Satellite hyperspectral imaging
  • Airborne hyperspectral cameras (UAV)
  • Spectroscopy of crop health status determination
  • Spectroscopy for crop phenotyping, germination, emergence and determination of the different growth stages of crops
  • Spectroscopy for detection of microorganism and pest management
  • Multisensor systems, sensor fusion
  • Non-destructive plant and vegetation spectroscopy
  • Yield estimation and prediction
  • Detection and identification of crops and weeds
  • Spectra analysis
  • Machine learning and emerging algorithms
  • Cloud computing
  • Real time processing
  • On-board processing
  • Multiple source data fusion
  • Hyperspectral data cubes
  • Precision agriculture applications
  • Monitoring water use and irrigation requirements
  • Crop damage assessment (frost, droughts, hail)
  • Site-specific applications and management of agricultural resources
  • Plant phenotyping
Dr. Thomas Alexandridis
Prof. Dimitrios Moshou
Guest Editor

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 papers will be 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 2200 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

  • Spectroscopic information acquisition
  • Spectral data processing
  • Spectroscopy in agriculture
  • Hyperspectral
  • Chemometrics
  • Bioinformatics
  • Precision agriculture
  • Phenotyping

Published Papers (2 papers)

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Research

Open AccessArticle
Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves
Remote Sens. 2020, 12(13), 2101; https://doi.org/10.3390/rs12132101 - 30 Jun 2020
Abstract
The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), [...] Read more.
The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight. Full article
(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)
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Open AccessArticle
Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy
Remote Sens. 2020, 12(12), 1920; https://doi.org/10.3390/rs12121920 - 13 Jun 2020
Abstract
Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory [...] Read more.
Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%. Full article
(This article belongs to the Special Issue Spectroscopic Analysis of Plants and Vegetation)
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