The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods
A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five diffe...
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6072 |
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| author | Marcin Siłuch Anna Siedliska Piotr Bartmiński Waldemar Kociuba Piotr Baranowski Jaromir Krzyszczak |
| author_facet | Marcin Siłuch Anna Siedliska Piotr Bartmiński Waldemar Kociuba Piotr Baranowski Jaromir Krzyszczak |
| author_sort | Marcin Siłuch |
| collection | DOAJ |
| description | A pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region. |
| first_indexed | 2024-03-11T03:58:31Z |
| format | Article |
| id | doaj.art-4188d1811a684c069f3f6d5642fb09a2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:31Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4188d1811a684c069f3f6d5642fb09a22023-11-18T00:20:08ZengMDPI AGApplied Sciences2076-34172023-05-011310607210.3390/app13106072The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging MethodsMarcin Siłuch0Anna Siedliska1Piotr Bartmiński2Waldemar Kociuba3Piotr Baranowski4Jaromir Krzyszczak5Department of Geology, Soil Science and Geoinformation, Institute of Earth and Environmental Sciences, Maria Curie-Skłodowska University, al. Kraśnicka 2cd, 20-718 Lublin, PolandInstitute of Agrophysics Polish, Academy of Sciences, ul. Doświadczalna 4, 20-290 Lublin, PolandDepartment of Geology, Soil Science and Geoinformation, Institute of Earth and Environmental Sciences, Maria Curie-Skłodowska University, al. Kraśnicka 2cd, 20-718 Lublin, PolandDepartment of Geology, Soil Science and Geoinformation, Institute of Earth and Environmental Sciences, Maria Curie-Skłodowska University, al. Kraśnicka 2cd, 20-718 Lublin, PolandInstitute of Agrophysics Polish, Academy of Sciences, ul. Doświadczalna 4, 20-290 Lublin, PolandInstitute of Agrophysics Polish, Academy of Sciences, ul. Doświadczalna 4, 20-290 Lublin, PolandA pot experiment was conducted to determine the impact of water availability on the discriminatory status of nitrogen (N) in plants using hyperspectral imaging. Nitrogen deficiency causes a significant decrease in chlorophyll concentration in plant leaves regardless of water availability. Five different classification algorithms were used to discriminate between nitrogen concentrations in plants at different levels of water availability. Several statistical parameters, including kappa and overall classification accuracy for calibration and prediction, were used to determine the efficiency and accuracy of the models. The Random Forest model had the highest overall accuracy of over 81% for sugar beet and over 78% for celery. Additionally, characteristic electromagnetic wavelengths were identified in which reflectance correlated with nitrogen and water content in plants could be recorded. It was also noted that the spectral resolution between the N and High Water (HW)/Low Water (LW) treatments was lower in the short-wave infrared (SWIR) region than in the visible and near-infrared (VNIR) region.https://www.mdpi.com/2076-3417/13/10/6072hyperspectral imagingnitrogen statusplant water stress |
| spellingShingle | Marcin Siłuch Anna Siedliska Piotr Bartmiński Waldemar Kociuba Piotr Baranowski Jaromir Krzyszczak The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods Applied Sciences hyperspectral imaging nitrogen status plant water stress |
| title | The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods |
| title_full | The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods |
| title_fullStr | The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods |
| title_full_unstemmed | The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods |
| title_short | The Impact of Water Availability on the Discriminative Status of Nitrogen (N) in Sugar Beet and Celery Using Hyperspectral Imaging Methods |
| title_sort | impact of water availability on the discriminative status of nitrogen n in sugar beet and celery using hyperspectral imaging methods |
| topic | hyperspectral imaging nitrogen status plant water stress |
| url | https://www.mdpi.com/2076-3417/13/10/6072 |
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