Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants
Water stress is a critical factor affecting the health and productivity of ornamental plants, yet early detection remains challenging. This study aims to investigate the spectral responses of four ornamental plant taxa—<i>Rosa</i> hybrid (rose), <i>Itea virginica</i> (itea),...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/285 |
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| author | Van Patiluna James Owen Joe Mari Maja Jyoti Neupane Jan Behmann David Bohnenkamp Irene Borra-Serrano José M. Peña James Robbins Ana de Castro |
| author_facet | Van Patiluna James Owen Joe Mari Maja Jyoti Neupane Jan Behmann David Bohnenkamp Irene Borra-Serrano José M. Peña James Robbins Ana de Castro |
| author_sort | Van Patiluna |
| collection | DOAJ |
| description | Water stress is a critical factor affecting the health and productivity of ornamental plants, yet early detection remains challenging. This study aims to investigate the spectral responses of four ornamental plant taxa—<i>Rosa</i> hybrid (rose), <i>Itea virginica</i> (itea), <i>Spiraea nipponica</i> (spirea), and <i>Weigela florida</i> (weigela)—under varying levels of water stress using hyperspectral imaging and principal component analysis (PCA). Hyperspectral data were collected across multiple wavelengths and PCA was applied to identify key spectral bands associated with different stress levels. The analyses revealed that the first two principal components captured a majority of variance in the data, with specific wavelengths around 680 nm, 760 nm, and 810 nm playing a significant role in distinguishing between the stress levels. Score plots demonstrated clear separation between different stress treatments, indicating that spectral signatures evolve distinctly over time as water stress progresses. Influence plots identified observations with disproportionate impacts on the PCA model, ensuring the robustness of the analysis. Findings suggest that hyperspectral imaging, combined with PCA, is a powerful tool for early detection and monitoring of water stress in ornamental plants, providing a basis for improved water management practices in horticulture. |
| format | Article |
| id | doaj-art-553ca831c7334e07aed431cb234c961f |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-553ca831c7334e07aed431cb234c961f2025-01-24T13:48:00ZengMDPI AGRemote Sensing2072-42922025-01-0117228510.3390/rs17020285Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental PlantsVan Patiluna0James Owen1Joe Mari Maja2Jyoti Neupane3Jan Behmann4David Bohnenkamp5Irene Borra-Serrano6José M. Peña7James Robbins8Ana de Castro9Center of Applied Artificial Intelligence for Sustainable Agriculture, 1890 Research and Extension, Public Service and Agriculture, South Carolina State University, 300 College Avenue, Orangeburg, SC 29117, USAApplication Technology Research Unit, United States Department of Agriculture, Agricultural Research Service, 1680 Madison Ave., Wooster, OH 44691, USACenter of Applied Artificial Intelligence for Sustainable Agriculture, 1890 Research and Extension, Public Service and Agriculture, South Carolina State University, 300 College Avenue, Orangeburg, SC 29117, USADepartment of Crop and Soil Sciences, Miller Plant Sciences, 120 Carlton Street, Athens, GA 30602, USAInstitute for Crop Science and Resource Conservation, Plant Diseases and Plant Protection, University of Bonn, 53115 Bonn, GermanyInstitute for Crop Science and Resource Conservation, Plant Diseases and Plant Protection, University of Bonn, 53115 Bonn, GermanyInstitute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), 28006 Madrid, SpainInstitute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), 28006 Madrid, SpainOrnamental Hort Solutions, 300 N Pine St, Little Rock, AR 72205, USAEnvironment and Agronomy Department, National Agricultural and Food Research and Technology Institute (INIA), Spanish National Research Council (CSIC), 28040 Madrid, SpainWater stress is a critical factor affecting the health and productivity of ornamental plants, yet early detection remains challenging. This study aims to investigate the spectral responses of four ornamental plant taxa—<i>Rosa</i> hybrid (rose), <i>Itea virginica</i> (itea), <i>Spiraea nipponica</i> (spirea), and <i>Weigela florida</i> (weigela)—under varying levels of water stress using hyperspectral imaging and principal component analysis (PCA). Hyperspectral data were collected across multiple wavelengths and PCA was applied to identify key spectral bands associated with different stress levels. The analyses revealed that the first two principal components captured a majority of variance in the data, with specific wavelengths around 680 nm, 760 nm, and 810 nm playing a significant role in distinguishing between the stress levels. Score plots demonstrated clear separation between different stress treatments, indicating that spectral signatures evolve distinctly over time as water stress progresses. Influence plots identified observations with disproportionate impacts on the PCA model, ensuring the robustness of the analysis. Findings suggest that hyperspectral imaging, combined with PCA, is a powerful tool for early detection and monitoring of water stress in ornamental plants, providing a basis for improved water management practices in horticulture.https://www.mdpi.com/2072-4292/17/2/285water stressornamental plantshyperspectral imagingprincipal component analysisprecision agriculture |
| spellingShingle | Van Patiluna James Owen Joe Mari Maja Jyoti Neupane Jan Behmann David Bohnenkamp Irene Borra-Serrano José M. Peña James Robbins Ana de Castro Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants Remote Sensing water stress ornamental plants hyperspectral imaging principal component analysis precision agriculture |
| title | Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants |
| title_full | Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants |
| title_fullStr | Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants |
| title_full_unstemmed | Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants |
| title_short | Using Hyperspectral Imaging and Principal Component Analysis to Detect and Monitor Water Stress in Ornamental Plants |
| title_sort | using hyperspectral imaging and principal component analysis to detect and monitor water stress in ornamental plants |
| topic | water stress ornamental plants hyperspectral imaging principal component analysis precision agriculture |
| url | https://www.mdpi.com/2072-4292/17/2/285 |
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