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Plants
Special Issues
Applications of Spectral Techniques in Plant Physiology
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Applications of Spectral Techniques in Plant Physiology

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1087

Special Issue Editor

Dr. Kumud Bandhu Mishra

E-Mail Website
Guest Editor
Laboratory of Ecological Plant Physiology, Czech Academy of Sciences, Global Change Research Institute, Bělidla 4a, 603 00 Brno, Czech Republic
Interests: spectroscopic techniques; abiotic stress; photosynthesis

Special Issue Information

Dear Colleagues，

The interaction of light with biological photosynthetic organisms results in physical phenomena such as scattering, reflection, absorption, and transmission; absorbed light oxidizes water molecules into molecular oxygen and protons and initiates photosynthesis via a series of highly complex photochemical and biochemical reactions; reflected light and scattered light are also highly informative and contain information on pigment compositions and/or surface structures. A range of spectroscopy techniques, with both non-imaging and imaging capabilities, have been widely used to unravel insights into photochemical, biochemical, and metabolic processes, as well as the mechanisms of plant photosynthetic reactions and their interactions with biotic and abiotic stresses on physiological states of the plants. This Special Issue will compile original research, reviews, perspectives on progress, technological breakthroughs, and challenges in the application of the spectroscopic techniques in plant physiology.

Dr. Kumud Bandhu Mishra
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

  • absorption spectroscopy
  • biochemistry
  • chlorophyll fluorescence
  • carotenoids
  • emission spectroscopy
  • excitation spectroscopy
  • imaging
  • phenotyping
  • photobiology
  • photochemistry
  • photoprotection
  • photosynthesis
  • pigments
  • plant growth and development
  • Raman spectroscopy
  • reflectance spectroscopy
  • stress tolerance
  • stress response
  • time-resolved spectroscopy

Published Papers (2 papers)

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Research

18 pages, 3258 KiB  
Article
Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll
by Ju Zhou, Feiyi Li, Xinwu Wang, Heng Yin, Wenjing Zhang, Jiaoyang Du and Haibo Pu
Plants 2024, 13(9), 1270; https://doi.org/10.3390/plants13091270 - 03 May 2024
Viewed by 348
Abstract
Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, [...] Read more.
Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture. Full article
(This article belongs to the Special Issue Applications of Spectral Techniques in Plant Physiology)
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20 pages, 6981 KiB  
Article
Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears
by Hongkun Ouyang, Lingling Tang, Jinglong Ma and Tao Pang
Plants 2024, 13(8), 1163; https://doi.org/10.3390/plants13081163 - 22 Apr 2024
Viewed by 357
Abstract
Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar [...] Read more.
Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG–GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG–SVR model. The SG–CARS–GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG–CARS–GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear. Full article
(This article belongs to the Special Issue Applications of Spectral Techniques in Plant Physiology)
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