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Zhuang J, Wang Q. Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters. PLANTS (BASEL, SWITZERLAND) 2024; 13:1923. [PMID: 39065450 PMCID: PMC11281006 DOI: 10.3390/plants13141923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024]
Abstract
Chlorophyll fluorescence (ChlF) parameters offer valuable insights into quantifying energy transfer and allocation at the photosystem level. However, tracking their variation based on reflectance spectral information remains challenging for large-scale remote sensing applications and ecological modeling. Spectral preprocessing methods, such as fractional-order derivatives (FODs), have been demonstrated to have advantages in highlighting spectral features. In this study, we developed and assessed the ability of novel spectral indices derived from FOD spectra and other spectral transformations to retrieve the ChlF parameters of various species and leaf groups. The results obtained showed that the empirical spectral indices were of low reliability in estimating the ChlF parameters. In contrast, the indices developed from low-order FOD spectra demonstrated a significant improvement in estimation. Furthermore, the incorporation of species specificity enhanced the tracking of the non-photochemical quenching (NPQ) of sunlit leaves (R2 = 0.61, r = 0.79, RMSE = 0.15, MAE = 0.13), the fraction of PSII open centers (qL) of shaded leaves (R2 = 0.50, r = 0.71, RMSE = 0.09, MAE = 0.08), and the fluorescence quantum yield (ΦF) of shaded leaves (R2 = 0.71, r = 0.85, RMSE = 0.002, MAE = 0.001). Our study demonstrates the potential of FOD spectra in capturing variations in ChlF parameters. Nevertheless, given the complexity and sensitivity of ChlF parameters, it is prudent to exercise caution when utilizing spectral indices for tracking them.
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Affiliation(s)
- Jie Zhuang
- Graduate School of Science and Technology, Shizuoka University, Shizuoka 422-8529, Japan;
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
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Falcioni R, Santos GLAAD, Crusiol LGT, Antunes WC, Chicati ML, Oliveira RBD, Demattê JAM, Nanni MR. Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy. PLANTS (BASEL, SWITZERLAND) 2023; 12:2526. [PMID: 37447089 DOI: 10.3390/plants12132526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/22/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
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 (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.
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Affiliation(s)
- Renan Falcioni
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
| | | | - Luis Guilherme Teixeira Crusiol
- Embrapa Soja (National Soybean Research Center-Brazilian Agricultural Research Corporation), Rodovia Carlos João Strass, s/nº, Distrito de Warta, Londrina 86001-970, Paraná, Brazil
| | - Werner Camargos Antunes
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
| | - Marcelo Luiz Chicati
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
| | - Roney Berti de Oliveira
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
| | - José A M Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil
| | - Marcos Rafael Nanni
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil
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Falcioni R, Antunes WC, Demattê JAM, Nanni MR. Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves. BIOLOGY 2023; 12:704. [PMID: 37237516 PMCID: PMC10215320 DOI: 10.3390/biology12050704] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023]
Abstract
The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques.
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Affiliation(s)
- Renan Falcioni
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (M.R.N.)
| | - Werner Camargos Antunes
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (M.R.N.)
| | - José A. M. Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil;
| | - Marcos Rafael Nanni
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (M.R.N.)
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Falcioni R, Antunes WC, Demattê JAM, Nanni MR. A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:3843. [PMID: 37112184 PMCID: PMC10143517 DOI: 10.3390/s23083843] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/03/2023] [Accepted: 04/08/2023] [Indexed: 06/01/2023]
Abstract
Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.
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Affiliation(s)
- Renan Falcioni
- Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil; (W.C.A.); (M.R.N.)
| | - Werner Camargos Antunes
- Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil; (W.C.A.); (M.R.N.)
| | - José Alexandre Melo Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of Sao Paulo, Av. Padua Dias, 11, Piracicaba 13418-260, Sao Paulo, Brazil;
| | - Marcos Rafael Nanni
- Department of Agronomy, State University of Maringa, Av. Colombo, 5790, Maringa 87020-900, Parana, Brazil; (W.C.A.); (M.R.N.)
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Falcioni R, Gonçalves JVF, de Oliveira KM, de Oliveira CA, Demattê JAM, Antunes WC, Nanni MR. Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms. PLANTS (BASEL, SWITZERLAND) 2023; 12:1333. [PMID: 36987021 PMCID: PMC10059284 DOI: 10.3390/plants12061333] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/03/2023] [Accepted: 03/08/2023] [Indexed: 06/19/2023]
Abstract
In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.
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Affiliation(s)
- Renan Falcioni
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - João Vitor Ferreira Gonçalves
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - Karym Mayara de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - Caio Almeida de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - José A. M. Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-260, São Paulo, Brazil;
| | - Werner Camargos Antunes
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
| | - Marcos Rafael Nanni
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (W.C.A.); (M.R.N.)
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6
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Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C. Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. FRONTIERS IN PLANT SCIENCE 2023; 14:1073346. [PMID: 36968402 PMCID: PMC10030857 DOI: 10.3389/fpls.2023.1073346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
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Affiliation(s)
- Mingzheng Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
| | - Tian’en Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Xiaohe Gu
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Dong Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Cong Wang
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Wenbiao Wu
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Chunjiang Zhao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
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Falcioni R, Moriwaki T, Gibin MS, Vollmann A, Pattaro MC, Giacomelli ME, Sato F, Nanni MR, Antunes WC. Classification and Prediction by Pigment Content in Lettuce ( Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy. PLANTS (BASEL, SWITZERLAND) 2022; 11:3413. [PMID: 36559526 PMCID: PMC9783279 DOI: 10.3390/plants11243413] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 05/14/2023]
Abstract
Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.
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Affiliation(s)
- Renan Falcioni
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Thaise Moriwaki
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Mariana Sversut Gibin
- Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Alessandra Vollmann
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Mariana Carmona Pattaro
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Marina Ellen Giacomelli
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Francielle Sato
- Optical Spectroscopy and Thermophysical Properties Research Group, Graduate Program in Physics, Department of Physics, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Marcos Rafael Nanni
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
| | - Werner Camargos Antunes
- Plant Ecophysiology Laboratory, Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Brazil
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