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Special Issue "Methodologies Used in Hyperspectral Remote Sensing in Agriculture"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 20 February 2024 | Viewed by 2490

Special Issue Editor

Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57007, USA
Interests: site-specific fertilizer management; using remote sensing data (satellite and drone images) for crop managements; using variouse spatial data (yield monitoring data, elevation data, soil data, RS data, soil test data) to describe spatial variability of production fields
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Special Issue Information

Dear Colleagues,

The new term in agriculture is ‘Digital Agriculture’. Data from traditional sampling in production fields are being saved in digital formats. Real-time field data collected by sensors is being saved in computer devices. Scouting data during the growing season is being uploaded to cloud servers directly from fields. Planting and harvesting data are being transferred to home computers and are uploaded onto cloud servers immediately. Digital platforms for agriculture gather data from producers, analyze accumulated data, display results, and give recommendations of better managements next year. Many producers and agronomists are using digital agriculture platforms not just for precision agriculture practices but for all kinds of field management. So, ‘Digital Agriculture’ has become a broader term than ‘Precision Agriculture’.

Common multispectral sensors that contain RGB, red-edge, and NIR wavelengths can detect crop plant healthiness using NDVI, NDRE, or other indicis. However, these indicis cannot differentiate between certain type of stresses. Specific wavelengths in hyperspectral sensors at specific times might be more sensitive in plants under a specific type of stress than other stresses and in soil under a specific property than others. Findings by hyperspectral sensors can be applied to make sensors that can detect specific targets.

Huge data analysis from hyperspectral sensors requires robust statistical and computational methods instead of simple linear regression analysis.

So, in the Special Issue ‘Methodologies Used in Hyperspectral Remote Sensing in Agriculture’, we welcome recent experimental research or cases studies such as statistical and computational (Artificially Intelligent) methods for hyperspectral data analysis to detect specific targets which includes:

  • different types of crop stress detection;
  • weed type differentiation;
  • crop type differentiation;
  • insect/pest infestation identification;
  • soil property and fertility sensing;
  • using different sensors including ground, UAV, airborne, and satellite platforms.

Dr. Jiyul Chang
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 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. Sensors 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 2600 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

  • hyperspectral sensor
  • artificial intelligence
  • machine learning
  • plant healthiness
  • plant stresses
  • fertilizer stress
  • water stress
  • pest infestation
  • insect infestation
  • soil property
  • crop type
  • weed type

Published Papers (2 papers)

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Research

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Article
Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing
Sensors 2023, 23(5), 2851; https://doi.org/10.3390/s23052851 - 06 Mar 2023
Viewed by 1436
Abstract
Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for [...] Read more.
Grapevine virus-associated disease such as grapevine leafroll disease (GLD) affects grapevine health worldwide. Current diagnostic methods are either highly costly (laboratory-based diagnostics) or can be unreliable (visual assessments). Hyperspectral sensing technology is capable of measuring leaf reflectance spectra that can be used for the non-destructive and rapid detection of plant diseases. The present study used proximal hyperspectral sensing to detect virus infection in Pinot Noir (red-berried winegrape cultivar) and Chardonnay (white-berried winegrape cultivar) grapevines. Spectral data were collected throughout the grape growing season at six timepoints per cultivar. Partial least squares-discriminant analysis (PLS-DA) was used to build a predictive model of the presence or absence of GLD. The temporal change of canopy spectral reflectance showed that the harvest timepoint had the best prediction result. Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay, respectively. Our results provide valuable information on the optimal time for GLD detection. This hyperspectral method can also be deployed on mobile platforms including ground-based vehicles and unmanned aerial vehicles (UAV) for large-scale disease surveillance in vineyards. Full article
(This article belongs to the Special Issue Methodologies Used in Hyperspectral Remote Sensing in Agriculture)
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Review

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Review
Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review
Sensors 2023, 23(12), 5366; https://doi.org/10.3390/s23125366 - 06 Jun 2023
Viewed by 658
Abstract
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this [...] Read more.
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection. Full article
(This article belongs to the Special Issue Methodologies Used in Hyperspectral Remote Sensing in Agriculture)
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