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Falcioni R, Antunes WC, Berti de Oliveira R, Chicati ML, Demattê JAM, Nanni MR. Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants. Cells 2024; 13:952. [PMID: 38891083 PMCID: PMC11171972 DOI: 10.3390/cells13110952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.
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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.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Werner Camargos Antunes
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Roney Berti de Oliveira
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Marcelo Luiz Chicati
- Department of Agronomy, State University of Maringá, Av. Colombo, 5790, Maringá 87020-900, Paraná, Brazil; (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - José Alexandre 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.); (R.B.d.O.); (M.L.C.); (M.R.N.)
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Falcioni R, Gonçalves JVF, de Oliveira KM, de Oliveira CA, Reis AS, Crusiol LGT, Furlanetto RH, Antunes WC, Cezar E, de Oliveira RB, Chicati ML, Demattê JAM, Nanni MR. Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy. PLANTS (BASEL, SWITZERLAND) 2023; 12:3424. [PMID: 37836163 PMCID: PMC10574701 DOI: 10.3390/plants12193424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023]
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 (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.
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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, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - João Vitor Ferreira Gonçalves
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Karym Mayara de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Caio Almeida de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Amanda Silveira Reis
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Luis Guilherme Teixeira Crusiol
- Embrapa Soja (National Soybean Research Centre–Brazilian Agricultural Research Corporation), Londrina 86001-970, PR, Brazil;
| | | | - Werner Camargos Antunes
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Everson Cezar
- Department of Agricultural and Earth Sciences, University of Minas Gerais State, Passos 37902-108, MG, Brazil;
| | - Roney Berti de Oliveira
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - Marcelo Luiz Chicati
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
| | - José Alexandre M. Demattê
- Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias 11, Piracicaba 13418-260, SP, Brazil;
| | - Marcos Rafael Nanni
- Graduate Program in Agronomy, Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, PR, Brazil; (J.V.F.G.); (K.M.d.O.); (C.A.d.O.); (A.S.R.); (W.C.A.); (R.B.d.O.); (M.L.C.); (M.R.N.)
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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:biology12050704. [PMID: 37237516 DOI: 10.3390/biology12050704] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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
| | - Werner Camargos Antunes
- 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. 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: 1] [Impact Index Per Article: 1.0] [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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Xu K, Ye H. Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves. Sci Rep 2023; 13:4694. [PMID: 36949090 PMCID: PMC10033640 DOI: 10.1038/s41598-023-31718-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 03/16/2023] [Indexed: 03/24/2023] Open
Abstract
Solar spectral reflectance and transmittance of natural leaves exhibit dramatic similarity. To elucidate the formation mechanism and physiological significance, a radiative transfer model was constructed, and the effects of stacked mesophyll cells, chlorophyll content and leaf thickness on the visible light absorptance of the natural leaves were analyzed. Results indicated that light scattering caused by the stacked mesophyll cells is responsible for the similarity. The optical path of visible light in the natural leaves is increased with the scattering process, resulting in that the visible light transmittance is significantly reduced meanwhile the visible light reflectance is at a low level, thus the visible light absorptance tends to a maximum and the absorption of photosynthetically active radiation (PAR) by the natural leaves is significantly enhanced. Interestingly, as two key leaf functional traits affecting the absorption process of PAR, chlorophyll content and leaf thickness of the natural leaves in a certain environment show a convergent behavior, resulting in the high visible light absorptance of the natural leaves, which demonstrates the PAR utilizing strategies of the natural leaves. This work provides a new perspective for revealing the evolutionary processes and ecological strategies of natural leaves, and can be adopted to guide the improvement directions of crop photosynthesis.
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Affiliation(s)
- Kai Xu
- Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, 230027, People's Republic of China
| | - Hong Ye
- Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, 230027, People's Republic of China.
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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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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:plants11243413. [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] [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
- Correspondence: ; Tel.: +55-44-3011-8940
| | - 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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Falcioni R, Moriwaki T, Antunes WC, Nanni MR. Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11182406. [PMID: 36145806 PMCID: PMC9501474 DOI: 10.3390/plants11182406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 05/14/2023]
Abstract
High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.
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Frisk CA, Xistris-Songpanya G, Osborne M, Biswas Y, Melzer R, Yearsley JM. Phenotypic variation from waterlogging in multiple perennial ryegrass varieties under climate change conditions. FRONTIERS IN PLANT SCIENCE 2022; 13:954478. [PMID: 35991411 PMCID: PMC9387306 DOI: 10.3389/fpls.2022.954478] [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: 05/27/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
Identifying how various components of climate change will influence ecosystems and vegetation subsistence will be fundamental to mitigate negative effects. Climate change-induced waterlogging is understudied in comparison to temperature and CO2. Grasslands are especially vulnerable through the connection with global food security, with perennial ryegrass dominating many flood-prone pasturelands in North-western Europe. We investigated the effect of long-term waterlogging on phenotypic responses of perennial ryegrass using four common varieties (one diploid and three tetraploid) grown in atmospherically controlled growth chambers during two months of peak growth. The climate treatments compare ambient climatological conditions in North-western Europe to the RCP8.5 climate change scenario in 2050 (+2°C and 550 ppm CO2). At the end of each month multiple phenotypic plant measurements were made, the plants were harvested and then allowed to grow back. Using image analysis and principal component analysis (PCA) methodologies, we assessed how multiple predictors (phenotypic, environmental, genotypic, and temporal) influenced overall plant performance, productivity and phenotypic responses. Long-term waterlogging was found to reduce leaf-color intensity, with younger plants having purple hues indicative of anthocyanins. Plant performance and yield was lower in waterlogged plants, with tetraploid varieties coping better than the diploid one. The climate change treatment was found to reduce color intensities further. Flooding was found to reduce plant productivity via reductions in color pigments and root proliferation. These effects will have negative consequences for global food security brought on by increased frequency of extreme weather events and flooding. Our imaging analysis approach to estimate effects of waterlogging can be incorporated into plant health diagnostics tools via remote sensing and drone-technology.
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Affiliation(s)
- Carl A. Frisk
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- Earth Institute, University College Dublin, Dublin, Ireland
| | | | - Matthieu Osborne
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Yastika Biswas
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Rainer Melzer
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- Earth Institute, University College Dublin, Dublin, Ireland
| | - Jon M. Yearsley
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- Earth Institute, University College Dublin, Dublin, Ireland
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The Feasibility of Leaf Reflectance-Based Taxonomic Inventories and Diversity Assessments of Species-Rich Grasslands: A Cross-Seasonal Evaluation Using Waveband Selection. REMOTE SENSING 2022. [DOI: 10.3390/rs14102310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and this finding implies challenges for the application of spectral variance in the estimation of biodiversity.
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Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression. REMOTE SENSING 2021. [DOI: 10.3390/rs13050977] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Soybean grain yield has regularly been impaired by drought periods, and the future climatic scenarios for soybean production might drastically impact yields worldwide. In this context, the knowledge of soybean yield is extremely important to subsidize government and corporative decisions over technical issues. This paper aimed to predict grain yield in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least square regression (PLSR). Field experiments were undertaken at Embrapa Soja (Brazilian Agricultural Research Corporation) in the 2016/2017, 2017/2018 and 2018/2019 cropping seasons. The data collected were analyzed following a split plot model in a randomized complete block design, with four blocks. The following water conditions were distributed in the field plots: irrigated (IRR), non-irrigated (NIRR) and water deficit induced at the vegetative (WDV) and reproductive stages (WDR) using rainout shelters. Soybean genotypes with different responses to water deficit were distributed in the subplots. Soil moisture and weather data were monitored daily. A total of 7216 leaf reflectance (from 400 to 2500 nm, measured by the FieldSpec 3 Jr spectroradiometer) was collected at 24 days in the three cropping seasons. The PLSR (p ≤ 0.05) was performed to predict soybean grain yield by its leaf-based reflectance spectroscopy. The results demonstrated the highest accuracy in soybean grain yield prediction at the R5 phenological stage, corresponding to the period when grains are being formed (R2 ranging from 0.731 to 0.924 and the RMSE from 334 to 403 kg ha−1—7.77 to 11.33%). Analyzing the three cropping seasons into a single PLSR model at R5 stage, R2 equal to 0.775, 0.730 and 0.688 were obtained at the calibration, cross-validation and external validation stages, with RMSE lower than 634 kg ha−1 (13.34%). The PLSR demonstrated higher accuracy in plants submitted to water deficit both at the vegetative and reproductive periods in comparison to plants under natural rainfall or irrigation.
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Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance. REMOTE SENSING 2021. [DOI: 10.3390/rs13020172] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.
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Gitelson A, Solovchenko A, Viña A. Foliar absorption coefficient derived from reflectance spectra: A gauge of the efficiency of in situ light-capture by different pigment groups. JOURNAL OF PLANT PHYSIOLOGY 2020; 254:153277. [PMID: 32979788 DOI: 10.1016/j.jplph.2020.153277] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/16/2020] [Accepted: 09/03/2020] [Indexed: 05/11/2023]
Abstract
The absorption of Photosynthetically Active Radiation (PAR) by different foliar pigments defines the amount of energy available for photosynthesis and also the need for photoprotection. Both characteristics reveal essential information about productivity, development, and stress acclimation of plants. Here we present an approach for the estimation of the efficiency by three foliar pigment groups (chlorophylls, carotenoids, and anthocyanins) at capturing light, via the absorption coefficient derived from leaf reflectance spectra. The absorption coefficient (and hence light capture efficiency) of the pigment is quantitatively related to the ratio of light absorbed by each pigment group over the total amount of light absorbed by the leaf. The proposed approach allows discerning the contribution of pigment groups to the overall light absorption, despite the strong interference by other pigments with overlapping absorption spectra. For photosynthetic pigments, like chlorophylls, this is indicative of the energy captured for photosynthesis and hence of potential plant productivity. For photoprotective pigments, like anthocyanins or secondary carotenoids, it gives information about the spectral ranges where their optical screening works best and their screening capacity. In addition, the approach allows the selection of optimal spectral bands where different pigments operate. Such information improves our understanding of the phenological, physiological and photosynthetic dynamics of plants over space and through time, useful for developing better monitoring and management strategies.
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Affiliation(s)
- Anatoly Gitelson
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA; Israel Institute of Technology (Technion), Haifa, 3200003, Israel.
| | - Alexei Solovchenko
- Departament of Bioengineering, Faculty of Biology, Lomonosov Moscow State University, 119234, Moscow, Russia; Laboratory of Precision Horticulture Technologies, Michurin Federal Scientific Centre, 393760, Michurinsk, Russia; Institute of Natural Sciences, Derzhavin Tambov State University, 392000, Tambov, Russia
| | - Andrés Viña
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, Lansing, MI, 48823, USA; Geography Department, University of North Carolina, Chapel Hill, NC, 27599, USA
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