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. 2023 Jan 26;12(3):561.
doi: 10.3390/plants12030561.

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones

Renata Andrade  1 , Sérgio Henrique Godinho Silva  1 , Lucas Benedet  1 , Elias Frank de Araújo  2 , Marco Aurélio Carbone Carneiro  1 , Nilton Curi  1
Affiliations

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones

Renata Andrade et al. Plants (Basel). .

Abstract

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.

Keywords: Eucalyptus cultivation; greentech analysis; leaf nutrient analysis; machine learning; plant mineral nutrition; portable X-ray fluorescence (pXRF) spectrometry; proximal sensing.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Principal component analysis (PCA) of portable X-ray fluorescence (pXRF) elemental data for Eucalyptus spp. leaves in different development stages.
Figure 2
Figure 2
Observed versus predicted scatter plots for the best macro and micronutrients prediction models of Eucalyptus spp. leaves based on portable X-ray fluorescence (pXRF) elemental data.
Figure 3
Figure 3
Confusion matrix for the best prediction models of Eucalyptus spp. leaves macro and micronutrients per class of nutrient status based on portable X-ray fluorescence (pXRF) elemental data. Darker colors indicate higher number of samples in that class.
Figure 4
Figure 4
Confusion matrix of Eucalyptus spp. clone prediction models based on portable X-ray fluorescence (pXRF) elemental data. Darker colors indicate higher number of samples in that class.
Figure 5
Figure 5
Explanatory variables used to predict the nutritional status of the Eucalyptus leaves and Eucalyptus spp. clones with their respective importance (%). X means that the variable was not used for the prediction model.
Figure 6
Figure 6
Overview of Eucalyptus spp. leaf collection and number of samples for each clone. C—Cambisol, F—Plintosol, G—Gleysol, M—Chernosol, N—Nitosol, P—Argisol, R—Neosol, S—Planosol, T—Luvosol.
Figure 7
Figure 7
Flowchart illustrating the sequence of the methodological process of this work for predicting the nutrient status of Eucalyptus spp. leaves and Eucalyptus spp. clones.
See this image and copyright information in PMC

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References

    1. IBÁ Brazilian Tree Industry Annual Report. 2021. [(accessed on 21 September 2022)]. Available online: https://iba.org/eng/datafiles/publicacoes/relatorios/relatorioiba2021.pdf.
    1. Vilasboa J., Da Costa C.T., Fett-Neto A.G. Environmental Modulation of Mini-Clonal Gardens for Cutting Production and Propagation of Hard- and Easy-to-Root Eucalyptus Spp. Plants. 2022;11:3281. doi: 10.3390/plants11233281. - DOI - PMC - PubMed
    1. Cagáň Ľ., Apacsová Fusková M., Hlávková D., Skoková Habuštová O. Essential Oils: Useful Tools in Storage-Pest Management. Plants. 2022;11:3077. doi: 10.3390/plants11223077. - DOI - PMC - PubMed
    1. Polito F., Kouki H., Khedhri S., Hamrouni L., Mabrouk Y., Amri I., Nazzaro F., Fratianni F., De Feo V. Chemical Composition and Phytotoxic and Antibiofilm Activity of the Essential Oils of Eucalyptus Bicostata, E. Gigantea, E. Intertexta, E. Obliqua, E. Pauciflora and E. Tereticornis. Plants. 2022;11:3017. doi: 10.3390/plants11223017. - DOI - PMC - PubMed
    1. Pinto M., Soares C., Martins M., Sousa B., Valente I., Pereira R., Fidalgo F. Herbicidal Effects and Cellular Targets of Aqueous Extracts from Young Eucalyptus Globulus Labill. Leaves. Plants. 2021;10:1159. doi: 10.3390/plants10061159. - DOI - PMC - PubMed

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