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Plants
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Integration of Spectroscopic and Photosynthetic Analyses in Plants
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Integration of Spectroscopic and Photosynthetic Analyses in Plants

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

Deadline for manuscript submissions: 28 June 2024 | Viewed by 3727

Special Issue Editors

Prof. Dr. Marcos Rafael Nanni

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Guest Editor
Full Professor and Researcher at the Department of Agronomy, University State of Maringá – UEM, Maringá, PR, Brazil
Interests: data fusion and processing; machine learning; multispectral and hyperspectral sensors; remote sensing; precision agriculture; UAV
Special Issues, Collections and Topics in MDPI journals
Dr. Renan Falcioni

E-Mail Website
Guest Editor
Affiliation: Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
Interests: biochemical and molecular analyses; chlorophyll a fluorescence; gas-exchange; plant phenotyping; photosynthesis; spectroscopy

Special Issue Information

Dear Colleagues,

In this Special Issue, "Integration of Spectroscopic and Photosynthetic Analyses in Plants," we emphasize the important convergence between spectroscopy and photosynthetic methods for analysis in plant research. This integration provides insights into plant tissues, physiological processes, and biochemical compositions, paving the way for a more comprehensive understanding of the mechanisms of plant biology.

The use of spectroscopic techniques, when aligned with different technologies, including multispectral, hyperspectral, and fluorescence imaging, promotes a better understanding and allows for non-invasive analyses of plants. This is essential for the assessment, classification, and prediction of plant responses to distinct environments. Moreover, aligning photosynthetic analyses with these methodologies can unlock profound insights into molecular photosynthetic mechanisms and ecosystem-level plant dynamics.

We also highlight the increasingly significant role of artificial intelligence, machine learning, and deep learning algorithms in accurately detecting changes induced by stresses, different treatments, and interactions across various contexts, such as laboratory experiments, greenhouses, fields, and environments associated with monitoring by remote sensing in plant research. These advanced computational tools are instrumental in managing and interpreting the extensive datasets generated by optical methods, thereby facilitating more precise and efficient correlations between spectroscopic and photosynthetic analyses.

All discussions and findings presented in this issue will significantly contribute to the ongoing progress of our understanding of plants. This comprehensive, integrative approach promises high-throughput breakthroughs in plant productivity, sustainability, and resilience, with the potential to transform both academic research and practical applications in plant biology.

In this issue, we will explore a wide range of topics, including but not limited to the following:

  • Integration of spectroscopy and photosynthesis analyses;
  • Applied spectroscopy in plant tissues;
  • Biophysical, physiological, biochemical, genetic, and molecular advances in relation to spectroscopy;
  • Chlorophyll a fluorescence and leaf optical properties;
  • Artificial intelligence, machine learning, and deep learning algorithms in plant research;
  • Development and application of chemometrics;
  • Classification and prediction parameters in plants;
  • Techniques and tools for plant phenotyping.

Prof. Dr. Marcos Rafael Nanni
Dr. Renan Falcioni
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. 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

  • applied spectroscopy in plant tissues
  • artificial intelligence, machine learning, and deep learning algorithms
  • chemometrics
  • chlorophyll a fluorescence
  • classification and prediction parameters
  • integration of biophysical, physiological, biochemical, genetic, and molecular advances with spectroscopy
  • leaf optical properties
  • photosynthesis analyses
  • plant phenotyping
  • yield of photosynthesis

Published Papers (3 papers)

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18 pages, 6543 KiB  
Article
UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe
by Xiaohua Zhu, Xinyu Chen, Lingling Ma and Wei Liu
Plants 2024, 13(7), 1006; https://doi.org/10.3390/plants13071006 - 31 Mar 2024
Viewed by 569
Abstract
Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, [...] Read more.
Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data. Full article
(This article belongs to the Special Issue Integration of Spectroscopic and Photosynthetic Analyses in Plants)
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24 pages, 27698 KiB  
Article
Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy
by Renan Falcioni, João Vitor Ferreira Gonçalves, Karym Mayara de Oliveira, Caio Almeida de Oliveira, Amanda Silveira Reis, Luis Guilherme Teixeira Crusiol, Renato Herrig Furlanetto, Werner Camargos Antunes, Everson Cezar, Roney Berti de Oliveira, Marcelo Luiz Chicati, José Alexandre M. Demattê and Marcos Rafael Nanni
Plants 2023, 12(19), 3424; https://doi.org/10.3390/plants12193424 - 28 Sep 2023
Cited by 1 | Viewed by 1059
Abstract
Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis [...] Read more.
Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms—such as PLS, VIP, iPLS-VIP, GA, RF, and CARS—the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation. Full article
(This article belongs to the Special Issue Integration of Spectroscopic and Photosynthetic Analyses in Plants)
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27 pages, 11829 KiB  
Article
Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy
by Renan Falcioni, Glaucio Leboso Alemparte Abrantes dos Santos, Luis Guilherme Teixeira Crusiol, Werner Camargos Antunes, Marcelo Luiz Chicati, Roney Berti de Oliveira, José A. M. Demattê and Marcos Rafael Nanni
Plants 2023, 12(13), 2526; https://doi.org/10.3390/plants12132526 - 02 Jul 2023
Cited by 4 | Viewed by 1610
Abstract
Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA [...] Read more.
Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson’s coefficient XYZ interpolations and HVI algorithms, the NIR−SWIR band combination proved the most effective for predicting height and leaf area, while VIS−NIR was optimal for optimal energy yield, and VIS−VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s−PLS were most significant for SWIR1 and SWIR2, while i−PLS showed a more uniform distribution in VIS−NIR−SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field. Full article
(This article belongs to the Special Issue Integration of Spectroscopic and Photosynthetic Analyses in Plants)
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