Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition
The decline of insect biomass, including pollinators, represents a significant ecological challenge, impacting both biodiversity and ecosystems. Effective monitoring of pollinator habitats, especially floral resources, is essential for addressing this issue. This study connects drone and deep learni...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1498913/full |
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| author | Marie Schnalke Jonas Funk Andreas Wagner Andreas Wagner |
| author_facet | Marie Schnalke Jonas Funk Andreas Wagner Andreas Wagner |
| author_sort | Marie Schnalke |
| collection | DOAJ |
| description | The decline of insect biomass, including pollinators, represents a significant ecological challenge, impacting both biodiversity and ecosystems. Effective monitoring of pollinator habitats, especially floral resources, is essential for addressing this issue. This study connects drone and deep learning technologies to their practical application in ecological research. It focuses on simplifying the application of these technologies. Updating an object detection toolbox to TensorFlow (TF) 2 enhanced performance and ensured compatibility with newer software packages, facilitating access to multiple object recognition models - Faster Region-based Convolutional Neural Network (Faster R-CNN), Single-Shot-Detector (SSD), and EfficientDet. The three object detection models were tested on two datasets of UAV images of flower-rich grasslands, to evaluate their application potential in practice. A practical guide for biologists to apply flower recognition to Unmanned Aerial Vehicle (UAV) imagery is also provided. The results showed that Faster RCNN had the best overall performance with a precision of 89.9% and a recall of 89%, followed by EfficientDet, which excelled in recall but at a lower precision. Notably, EfficientDet demonstrated the lowest model complexity, making it a suitable choice for applications requiring a balance between efficiency and detection performance. Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability. |
| format | Article |
| id | doaj-art-c1241fa8062e4ec79cead31bf4ffc3d4 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-c1241fa8062e4ec79cead31bf4ffc3d42025-08-20T02:56:36ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-03-011610.3389/fpls.2025.14989131498913Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognitionMarie Schnalke0Jonas Funk1Andreas Wagner2Andreas Wagner3Faculty of Management Science and Engineering, Karlsruhe University of Applied Sciences (HKA), Karlsruhe, GermanyFaculty of Management Science and Engineering, Karlsruhe University of Applied Sciences (HKA), Karlsruhe, GermanyFaculty of Management Science and Engineering, Karlsruhe University of Applied Sciences (HKA), Karlsruhe, GermanyFraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, GermanyThe decline of insect biomass, including pollinators, represents a significant ecological challenge, impacting both biodiversity and ecosystems. Effective monitoring of pollinator habitats, especially floral resources, is essential for addressing this issue. This study connects drone and deep learning technologies to their practical application in ecological research. It focuses on simplifying the application of these technologies. Updating an object detection toolbox to TensorFlow (TF) 2 enhanced performance and ensured compatibility with newer software packages, facilitating access to multiple object recognition models - Faster Region-based Convolutional Neural Network (Faster R-CNN), Single-Shot-Detector (SSD), and EfficientDet. The three object detection models were tested on two datasets of UAV images of flower-rich grasslands, to evaluate their application potential in practice. A practical guide for biologists to apply flower recognition to Unmanned Aerial Vehicle (UAV) imagery is also provided. The results showed that Faster RCNN had the best overall performance with a precision of 89.9% and a recall of 89%, followed by EfficientDet, which excelled in recall but at a lower precision. Notably, EfficientDet demonstrated the lowest model complexity, making it a suitable choice for applications requiring a balance between efficiency and detection performance. Challenges remain, such as detecting flowers in dense vegetation and accounting for environmental variability.https://www.frontiersin.org/articles/10.3389/fpls.2025.1498913/fullflower detectiondeep learningunmanned aerial vehicle (UAV)biodiversityremote sensing |
| spellingShingle | Marie Schnalke Jonas Funk Andreas Wagner Andreas Wagner Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition Frontiers in Plant Science flower detection deep learning unmanned aerial vehicle (UAV) biodiversity remote sensing |
| title | Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition |
| title_full | Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition |
| title_fullStr | Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition |
| title_full_unstemmed | Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition |
| title_short | Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition |
| title_sort | bridging technology and ecology enhancing applicability of deep learning and uav based flower recognition |
| topic | flower detection deep learning unmanned aerial vehicle (UAV) biodiversity remote sensing |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1498913/full |
| work_keys_str_mv | AT marieschnalke bridgingtechnologyandecologyenhancingapplicabilityofdeeplearninganduavbasedflowerrecognition AT jonasfunk bridgingtechnologyandecologyenhancingapplicabilityofdeeplearninganduavbasedflowerrecognition AT andreaswagner bridgingtechnologyandecologyenhancingapplicabilityofdeeplearninganduavbasedflowerrecognition AT andreaswagner bridgingtechnologyandecologyenhancingapplicabilityofdeeplearninganduavbasedflowerrecognition |