Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower
Sunflower broomrape (Orobanche cumana) poses a severe threat to sunflower crops, parasitizing their roots and hindering plant growth. Current control methods, which typically rely on uniform herbicide applications, are economically inefficient and environmentally damaging. This study investigates th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525000437 |
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| author | Guy Atsmon Anna Brook Tom Avikasis Cohen Fadi Kizel Hanan Eizenberg Ran Nisim Lati |
| author_facet | Guy Atsmon Anna Brook Tom Avikasis Cohen Fadi Kizel Hanan Eizenberg Ran Nisim Lati |
| author_sort | Guy Atsmon |
| collection | DOAJ |
| description | Sunflower broomrape (Orobanche cumana) poses a severe threat to sunflower crops, parasitizing their roots and hindering plant growth. Current control methods, which typically rely on uniform herbicide applications, are economically inefficient and environmentally damaging. This study investigates the use of unmanned aerial vehicle (UAV)-based multispectral imaging to detect broomrape-infected sunflowers by analyzing temporal patterns in spectral vegetation indices (VIs). Over four imaging campaigns conducted during early subsoil parasitic stages, multispectral data were collected and processed to compute ten VIs. These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). Results showed that the PRNN model, trained on time-series data, achieved an overall accuracy of 84.8 % and a true positive rate of 80.4 % in detecting broomrape infection, emphasizing the strength of utilizing temporal data for enhancing detection accuracies. Pixel-level reconstruction maps revealed varying spectral responses within infected canopies, highlighting the importance of accounting for this heterogeneity. This study demonstrates the potential of UAV-based multispectral imaging combined with advanced machine learning (ML) techniques for early detection of broomrape infestations in sunflower crops, offering insights for managing similar infestations in other crops. |
| format | Article |
| id | doaj-art-a33ad32b86d444bd8ac33f2e22835659 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-a33ad32b86d444bd8ac33f2e228356592025-02-08T05:01:38ZengElsevierSmart Agricultural Technology2772-37552025-03-0110100809Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflowerGuy Atsmon0Anna Brook1Tom Avikasis Cohen2Fadi Kizel3Hanan Eizenberg4Ran Nisim Lati5Department of Plant Pathology and Weed Research, Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay 30095, Israel; The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, Israel; Corresponding author.Spectroscopy & Remote Sensing Laboratory, The School of Environmental Sciences, University of Haifa, Mount Carmel 3498838, IsraelSpectroscopy & Remote Sensing Laboratory, The School of Environmental Sciences, University of Haifa, Mount Carmel 3498838, IsraelDepartment of Mapping and Geo-Information Engineering, Technion–Israel Institute of Technology, Haifa, IsraelDepartment of Plant Pathology and Weed Research, Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay 30095, Israel; The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 7610001, IsraelDepartment of Plant Pathology and Weed Research, Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishay 30095, IsraelSunflower broomrape (Orobanche cumana) poses a severe threat to sunflower crops, parasitizing their roots and hindering plant growth. Current control methods, which typically rely on uniform herbicide applications, are economically inefficient and environmentally damaging. This study investigates the use of unmanned aerial vehicle (UAV)-based multispectral imaging to detect broomrape-infected sunflowers by analyzing temporal patterns in spectral vegetation indices (VIs). Over four imaging campaigns conducted during early subsoil parasitic stages, multispectral data were collected and processed to compute ten VIs. These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). Results showed that the PRNN model, trained on time-series data, achieved an overall accuracy of 84.8 % and a true positive rate of 80.4 % in detecting broomrape infection, emphasizing the strength of utilizing temporal data for enhancing detection accuracies. Pixel-level reconstruction maps revealed varying spectral responses within infected canopies, highlighting the importance of accounting for this heterogeneity. This study demonstrates the potential of UAV-based multispectral imaging combined with advanced machine learning (ML) techniques for early detection of broomrape infestations in sunflower crops, offering insights for managing similar infestations in other crops.http://www.sciencedirect.com/science/article/pii/S2772375525000437ClassificationMultispectralSite-specific weed managementPattern recognition neural network |
| spellingShingle | Guy Atsmon Anna Brook Tom Avikasis Cohen Fadi Kizel Hanan Eizenberg Ran Nisim Lati Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower Smart Agricultural Technology Classification Multispectral Site-specific weed management Pattern recognition neural network |
| title | Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower |
| title_full | Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower |
| title_fullStr | Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower |
| title_full_unstemmed | Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower |
| title_short | Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower |
| title_sort | leveraging time based spectral data from uav imagery for enhanced detection of broomrape in sunflower |
| topic | Classification Multispectral Site-specific weed management Pattern recognition neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525000437 |
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