Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging
Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle....
| Main Authors: | , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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MDPI
2023
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| Online Access: | https://hdl.handle.net/20.500.11939/8696 https://www.mdpi.com/2077-0472/13/4/916 |
| _version_ | 1855492541633265664 |
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| author | Acosta, Maylin Rodríguez-Carretero, Isabel Blasco, José De-Paz, José M. Quinones, Ana |
| author_browse | Acosta, Maylin Blasco, José De-Paz, José M. Quinones, Ana Rodríguez-Carretero, Isabel |
| author_facet | Acosta, Maylin Rodríguez-Carretero, Isabel Blasco, José De-Paz, José M. Quinones, Ana |
| author_sort | Acosta, Maylin |
| collection | ReDivia |
| description | Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The average reflectance spectrum of each leaf was extracted, and foliar ionomic analysis was used as a reference method to determine the actual concentration of the nutrients in the leaves. Analyses were performed via emission spectrometry (ICP-OES) for macro- and micronutrients after microwave digestion and using the Kjeldahl method to quantify nitrogen. Partial least square regression (PLS-R) was used to predict the nutrient concentration based on spectral data from the leaf using actual values of each element as predictor variables. Several methods were used to pre-process the spectra, including Savitzky–Golay (SG) smoothing, standard normal variate (SNV) and first (1D) and second derivatives (2D). Seventy-five percent of the samples were used to calibrate and validate the model by cross-validation, whereas the remaining twenty-five % were used as an independent test set. The best performance of the models for the test set achieved an R2 = 0.80 for nitrogen. Results were also satisfactory for phosphorous, calcium, magnesium and boron, with determination coefficient R2 values of 0.63, 0.66, 0.58 and 0.69, respectively. For the other nutrients, lower prediction rates were attained (R2 = 0.48 for potassium, R2 = 0.38 for iron, R2 = 0.24 for copper, R2 = 0.23 for zinc and R2 = 0.22 for manganese). The variable importance in projection (VIP) was used to extract the most influential bands for the best-predicted nutrients, which were N, K and B. |
| format | Artículo |
| id | ReDivia8696 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia86962025-04-25T14:49:19Z Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging Acosta, Maylin Rodríguez-Carretero, Isabel Blasco, José De-Paz, José M. Quinones, Ana Hyperspectral imaging Vis/NIR Chemometrics Variable selection Non-invasive techniques F04 Fertilizing F61 Plant physiology - Nutrition U30 Research methods U40 Surveying methods N20 Agricultural machinery and equipment Spectroscopy Micronutrients Macronutrients Diospyros kaki Leaf analysis Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The average reflectance spectrum of each leaf was extracted, and foliar ionomic analysis was used as a reference method to determine the actual concentration of the nutrients in the leaves. Analyses were performed via emission spectrometry (ICP-OES) for macro- and micronutrients after microwave digestion and using the Kjeldahl method to quantify nitrogen. Partial least square regression (PLS-R) was used to predict the nutrient concentration based on spectral data from the leaf using actual values of each element as predictor variables. Several methods were used to pre-process the spectra, including Savitzky–Golay (SG) smoothing, standard normal variate (SNV) and first (1D) and second derivatives (2D). Seventy-five percent of the samples were used to calibrate and validate the model by cross-validation, whereas the remaining twenty-five % were used as an independent test set. The best performance of the models for the test set achieved an R2 = 0.80 for nitrogen. Results were also satisfactory for phosphorous, calcium, magnesium and boron, with determination coefficient R2 values of 0.63, 0.66, 0.58 and 0.69, respectively. For the other nutrients, lower prediction rates were attained (R2 = 0.48 for potassium, R2 = 0.38 for iron, R2 = 0.24 for copper, R2 = 0.23 for zinc and R2 = 0.22 for manganese). The variable importance in projection (VIP) was used to extract the most influential bands for the best-predicted nutrients, which were N, K and B. 2023-08-29T11:20:30Z 2023-08-29T11:20:30Z 2023 article publishedVersion Acosta, M., Rodríguez-Carretero, I., Blasco, J., de-Paz, J. M. & Quiñones, A. (2023). Non-Destructive Appraisal of Macro-and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging. Agriculture, 13(4), 916. 2077-0472 https://hdl.handle.net/20.500.11939/8696 10.3390/agriculture13040916 https://www.mdpi.com/2077-0472/13/4/916 en This work is co-funded by MICIN-AEI through project TED2021-130117B-C31, GVA-IVIA through projects 52203 and 52204, and the EU through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2021–2027. info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52203/ES/Sostenibilidad y economía circular como ejes de desarrollo del sector agrario valenciano: suelo, agua y biodiversidad/SostE-SABio info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52204/ES/Tecnología inteligente para una agricultura digital, sostenible y precisa en la comunitat valenciana/AgrIntel·ligència-CV info:eu-repo/grantAgreement/MICIN//TED2021-130117B-C31/ES/Smart autonomous electrical robot for a digital and sustainable agriculture in the Valencian Community/AgriSmartRobot Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess MDPI electronico |
| spellingShingle | Hyperspectral imaging Vis/NIR Chemometrics Variable selection Non-invasive techniques F04 Fertilizing F61 Plant physiology - Nutrition U30 Research methods U40 Surveying methods N20 Agricultural machinery and equipment Spectroscopy Micronutrients Macronutrients Diospyros kaki Leaf analysis Acosta, Maylin Rodríguez-Carretero, Isabel Blasco, José De-Paz, José M. Quinones, Ana Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging |
| title | Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging |
| title_full | Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging |
| title_fullStr | Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging |
| title_full_unstemmed | Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging |
| title_short | Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging |
| title_sort | non destructive appraisal of macro and micronutrients in persimmon leaves using vis nir hyperspectral imaging |
| topic | Hyperspectral imaging Vis/NIR Chemometrics Variable selection Non-invasive techniques F04 Fertilizing F61 Plant physiology - Nutrition U30 Research methods U40 Surveying methods N20 Agricultural machinery and equipment Spectroscopy Micronutrients Macronutrients Diospyros kaki Leaf analysis |
| url | https://hdl.handle.net/20.500.11939/8696 https://www.mdpi.com/2077-0472/13/4/916 |
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