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Application of Hyperspectral Remote Sensing Technology in Plant Research
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Application of Hyperspectral Remote Sensing Technology in Plant Research

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1425

Special Issue Editor

Dr. Yuan Zhang

E-Mail Website
Guest Editor
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
Interests: hyperspectral remote sensing; carbon–water estimation in ecosystems; ecosystem monitoring and assessment

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing is a method that acquires spectrally continuous image data with a narrow bandwidth (nanometer scale) across various bands of the electromagnetic spectrum, including visible, near-infrared, mid-infrared, and thermal infrared. This technology enables the concurrent collection of spatial, radiative, and spectral information about objects on the ground. Compared with traditional remote sensing technology, hyperspectral remote sensing offers richer spectral information for the identification of ground objects and the analysis of their components. This advancement enables remote sensing to shift from qualitative to quantitative or semi-quantitative analysis. Thus, the application of hyperspectral remote sensing technology has significantly advanced research in the field of plant studies.

This Special Issue will focus on the application of hyperspectral remote sensing technology (including near-ground hyperspectral and satellite hyperspectral) in plant research at the scale of leaf, community, and landscape. Both original research articles and review articles are welcome. Specific topics of interest include, but are not limited to, the following:

  • Identification and classification of vegetation (types of crops and forests, etc.);
  • Monitoring and mapping of vegetation (environmental stress, pest and disease, forest loss caused by man-made and natural causes, etc.);
  • Quantitative inversion of plant functional traits (carbon, nitrogen, and water content, photosynthetic capacity, diversity, etc.).

Dr. Yuan Zhang
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. Plants 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

  • hyperspectral remote sensing
  • identification and classification
  • monitoring and mapping
  • quantitative inversion
  • plant functional traits

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

14 pages, 2228 KiB  
Article
Small-Sample Authenticity Identification and Variety Classification of Anoectochilus roxburghii (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning
by Yiqing Xu, Haoyuan Ding, Tingsong Zhang, Zhangting Wang, Hongzhen Wang, Lu Zhou, Yujia Dai and Ziyuan Liu
Plants 2025, 14(8), 1177; https://doi.org/10.3390/plants14081177 - 10 Apr 2025
Viewed by 264
Abstract
This study aims to utilize hyperspectral imaging technology combined with machine learning methods for the authenticity identification and classification of Anoectochilus roxburghii and its counterfeit species. Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two [...] Read more.
This study aims to utilize hyperspectral imaging technology combined with machine learning methods for the authenticity identification and classification of Anoectochilus roxburghii and its counterfeit species. Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two counterfeit species (Bloodleaf and Spotted-leaf), followed by classification using a variety of machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN). The experimental results demonstrated that the SVM model achieved 100% classification accuracy for distinguishing Goldthread from its counterfeit species, effectively capturing the spectral differences between the front and back leaves. In contrast, traditional machine learning models showed varied performance, with SVM proving superior due to its ability to handle high-dimensional feature spaces. The introduction of a multi-view spectral fusion CNN model, which integrates spectral data from both the front and back leaves, further enhanced classification accuracy, achieving a perfect classification rate of 100%. This approach highlights the potential of hyperspectral imaging and machine learning in plant authenticity identification and provides a new perspective for the detection of counterfeit species. Full article
(This article belongs to the Special Issue Application of Hyperspectral Remote Sensing Technology in Plant Research)
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15 pages, 4664 KiB  
Article
Research on Lettuce Canopy Image Processing Method Based on Hyperspectral Imaging Technology
by Chao Chen, Yue Jiang and Xiaoqing Zhu
Plants 2024, 13(23), 3403; https://doi.org/10.3390/plants13233403 - 4 Dec 2024
Viewed by 801
Abstract
For accurate segmentation of lettuce canopy images, dealing with uneven illumination and background interference, hyperspectral imaging technology was applied to capture images of lettuce from the rosette to nodule stages. The spectral ratio method was used to select the characteristic wavelengths, and the [...] Read more.
For accurate segmentation of lettuce canopy images, dealing with uneven illumination and background interference, hyperspectral imaging technology was applied to capture images of lettuce from the rosette to nodule stages. The spectral ratio method was used to select the characteristic wavelengths, and the characteristic wavelength images were denoised and image fused before being processed by filtering and threshold segmentation. To verify the accuracy of this segmentation method, the manual segmentation method and the segmentation method used in this study were compared, and the area overlap degree (AOM) and misclassification rate (ME) were used as criteria to evaluate the segmentation results. The results showed that the segmentation effect was the best when 553.8 nm, 702.5 nm and 731.3 nm were selected as the characteristic wavelengths of lettuce for the spectral ratio method, with an AOM of 0.9526 and an ME of 0.0477. Both have a variance of less than 0.01 and have the best stability. Hyperspectral imaging technology combined with multi-wavelength image and multi-threshold segmentation can achieve accurate segmentation of lettuce canopy images. Full article
(This article belongs to the Special Issue Application of Hyperspectral Remote Sensing Technology in Plant Research)
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