Nutrient deficiency lowers photochemical and carboxylation efficiency in tobacco

Falcioni, Renan; Moriwaki, Thaise; Rodrigues, Marlon; Oliveira, Karym Mayara de; Furlanetto, Renato Herrig; Reis, Amanda Silveira; Santos, Gláucio Leboso Alemparte Abrantes dos; Mendonça, Weslei Augusto; Crusiol, Luís Guilherme Teixeira; Gonçalves, João Vitor Ferreira; Chicati, Marcelo Luiz; Oliveira, Roney Berti de; Nanni, Marcos Rafael; Antunes, Werner Camargos

doi:10.1007/s40626-023-00268-2

Cited by 12 publications

(17 citation statements)

References 62 publications

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“…Despite this, the results still exhibit a higher level of accuracy for Chl a+b, compared to data obtained through PLSR. These findings demonstrate the potential of combining hyperspectral data with PLSR for the study of leaf pigments [10,42,74]. Most previous studies have only focused on reflectance curves, limiting their ability to fully comprehend the spectral interactions that lead to accurate pigment estimation.…”

Section: Prediction Based On An Independent Data Setmentioning

confidence: 84%

“…On the other hand, leaves grown in shaded environments have a lighter color, more chlorophyll by mass, higher reflectance, and lower absorbance (Figure 7). In this sense, leaves grown under high light conditions show higher levels of absorbance than those grown under low light conditions [8,[40][41][42]. Therefore, changes in leaf structure and ultrastructure can affect leaf absorption spectra and have a stronger correlation with leaf optical properties and the estimation of photosynthetic pigments compared to the reflective characteristics of the epidermis (Figure 7) or following reported estimates obtained from reflectance data [18,43].…”

Section: Structure Ultrastructure and Photosynthetic Pigmentsmentioning

confidence: 91%

“…The models for predicting Chl a+b and Car concentrations were accurate and had excellent correlations in the spectral range (0.2 to 0.6 g m −2 for Chl a+b and 0.04 to 0.16 g m −2 for Car, Figure 7, Figure 9, Figure 10, and Figure 12). The absorbance data based on two sensors were considered to better reflect the relationship between light and pigments in the thylakoid membranes, being closer to in vivo events [10,42,74]. The cross-validation estimate for the reflectance data was also found to be better than the estimate using an independent data set.…”

Section: Prediction Based On An Independent Data Setmentioning

confidence: 99%

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A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Falcioni

Antunes

Demattê

et al. 2023

Sensors

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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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Section: Prediction Based On An Independent Data Setmentioning

confidence: 84%

Section: Structure Ultrastructure and Photosynthetic Pigmentsmentioning

confidence: 91%

Section: Prediction Based On An Independent Data Setmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Falcioni

Antunes

Demattê

et al. 2023

Sensors

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“…The absorption of electromagnetic radiation is directly related to components such as phenolic compounds, flavonoids, anthocyanins, carotenoids, and chlorophyll concentration [ 64 ]. Many of these compounds, such as chlorophyll, are biochemical indicators strictly related to the N content in the plants [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Fiorio,

Silva,

Rizzo

et al. 2024

Heliyon

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“…The visible region of the electromagnetic spectrum is strongly influenced by the presence of photosynthetic pigments, including chlorophylls "a" and "b", and carotenoids, such as carotenes and xanthophylls [18,21,44,45]. Plants susceptible to K + deficiency exhibit lower chlorophyll "a" and "b" concentrations, higher stomatal restriction, and lower electron transition compared to tolerant plants, as mentioned in the study conducted by Zhao et al [46].…”

Section: Visual Analysis Of the Reflectance Spectramentioning

confidence: 97%

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)

Furlanetto,

Crusiol,

Nanni

et al. 2024

Remote Sensing

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Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves.

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Nutrient deficiency lowers photochemical and carboxylation efficiency in tobacco

Cited by 12 publications

References 62 publications

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)

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