Flower visitation through the lens: Exploring the foraging behaviour of Bombus terrestris with a computer vision-based application DOI
Zsófia Varga‐Szilay, Gergely Szövényi, Gábor Pozsgai

et al.

Published: July 15, 2024

Abstract To understand the processes behind pollinator declines, and thus to maintain pollination efficiency, we also have fundamental drivers influencing behaviour. In this study, aim explore foraging behaviour of wild bumblebees, recognizing its importance from economic conservation perspectives. We recorded Bombus terrestris on Lotus creticus , Persicaria capitata Trifolium pratense patches in five-minute-long slots urban areas Terceira (Azores, Portugal). For automated bumblebee detection, created computer vision models based a deep learning algorithm, with custom datasets. achieved high F1 scores 0.88 for 0.95 indicating accurate detection. found that flower cover per cent, but not plant species, influenced attractiveness patches, significant positive effect. There were no differences between species heads. The handling time was longer large-headed than those smaller-headed . However, our result did indicate bumblebees spent flowers among three species. Here, justify vision-based analysis as reliable tool studying behavioural ecology.

Language: Английский

Insect detect: An open-source DIY camera trap for automated insect monitoring DOI Creative Commons
Maximilian Sittinger, Johannes Uhler, M. A. Pink

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(4), P. e0295474 - e0295474

Published: April 3, 2024

Insect monitoring is essential to design effective conservation strategies, which are indispensable mitigate worldwide declines and biodiversity loss. For this purpose, traditional methods widely established can provide data with a high taxonomic resolution. However, processing of captured insect samples often time-consuming expensive, limits the number potential replicates. Automated facilitate collection at higher spatiotemporal resolution comparatively lower effort cost. Here, we present Detect DIY (do-it-yourself) camera trap for non-invasive automated flower-visiting insects, based on low-cost off-the-shelf hardware components combined open-source software. Custom trained deep learning models detect track insects landing an artificial flower platform in real time on-device subsequently classify cropped detections local computer. Field deployment solar-powered confirmed its resistance temperatures humidity, enables autonomous during whole season. On-device detection tracking estimate activity/abundance after metadata post-processing. Our classification model achieved top-1 accuracy test dataset generalized well real-world images. The software highly customizable be adapted different use cases. With custom models, as accessible programming, many possible applications surpassing our proposed method realized.

Language: Английский

Citations

12
Utilising affordable smartphones and open-source time-lapse photography for pollinator image collection and annotation DOI Creative Commons
Valentin Ştefan,

Aspen Workman,

Jared C. Cobain

et al.

Journal of Pollination Ecology, Journal Year: 2025, Volume and Issue: 37, P. 1 - 21

Published: Jan. 10, 2025

Monitoring plant-pollinator interactions is crucial for understanding the factors influencing these relationships across space and time. Traditional methods in pollination ecology are resource-intensive, while time-lapse photography offers potential non-destructive automated complementary techniques. However, accurate identification of pollinators at finer taxonomic levels (i.e., genus or species) requires high enough image quality. This study assessed feasibility using a smartphone setup to capture images arthropods visiting flowers evaluated whether offered sufficient resolution arthropod by taxonomists. Smartphones were positioned above target from various plant species urban green areas around Leipzig Halle, Germany. We present proportions identifications (instances) different (order, family, genus, based on visible features as interpreted document limitations stem (e.g., fixed positioning preventing distinguishing despite resolution) low Recommendations provided address challenges. Our results indicate that 89.81% all Hymenoptera instances identified family level, 84.56% pollinator only 25.35% level. less able identify Dipterans levels, with nearly 50% not identifiable 26.18% 15.19% levels. was due their small size more challenging needed wing veins). Advancing technology, along accessibility, affordability, user-friendliness, promising option coarse-level monitoring.

Language: Английский

Citations

2
Emerging technologies for pollinator monitoring DOI
Toke T. Høye, Matteo Montagna, Bas Oteman

et al.

Current Opinion in Insect Science, Journal Year: 2025, Volume and Issue: unknown, P. 101367 - 101367

Published: March 1, 2025

Language: Английский

Citations

2
Best practices for long-term monitoring of island arthropods: insights from the Azores Islands DOI Creative Commons
Paulo A. V. Borges

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: March 17, 2025

Language: Английский

Citations

1
A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments DOI Creative Commons
Kim Bjerge, Henrik Karstoft, Hjalte M. R. Mann

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102861 - 102861

Published: Oct. 1, 2024

Language: Английский

Citations

6
In-field monitoring of ground-nesting insect aggregations using a scaleable multi-camera system DOI Creative Commons
Daniela Calvus, Karoline Wueppenhorst,

R.E. Schlosser

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103004 - 103004

Published: Jan. 1, 2025

Language: Английский

Citations

0
Sticky Trap-Embedded Machine Vision for Tea Pest Monitoring: A Cross-Domain Transfer Learning Framework Addressing Few-Shot Small Target Detection DOI Creative Commons
Kunhong Li,

Yi Li,

Xuan Wen

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 693 - 693

Published: March 13, 2025

Pest infestations have always been a major factor affecting tea production. Real-time detection of pests using machine vision is mainstream method in modern agricultural pest control. Currently, there notable absence devices capable real-time monitoring for small-sized the market, and scarcity open-source datasets available remains critical limitation. This manuscript proposes YOLOv8-FasterTea algorithm based on cross-domain transfer learning, which was successfully deployed novel device. The proposed leverages learning from natural language character domain to domain, termed complex small characteristics shared by characters pests. With sufficient samples can effectively enhance tiny feature extraction capabilities deep networks mitigate few-shot problem detection. information texture features are more likely be lost with layers neural network becoming deep. Therefore, method, YOLOv8-FasterTea, removes P5 layer adds P2 target YOLOv8 model. Additionally, original C2f module replaced lighter convolutional modules reduce loss about Finally, this applies outdoor equipment. Experimental results demonstrate that, sample yellow board dataset, [email protected] value model increased approximately 6%, average, after learning. improved 3.7%, while size reduced 46.6%.

Language: Английский

Citations

0
Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring DOI
Valentin Ştefan, Thomas Stark, Michael Wurm

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Abstract Pollinating insects provide essential ecosystem services, and using time-lapse photography to automate their observation could improve monitoring efficiency. Computer vision models, trained on clear citizen science photos, can detect in similar images with high accuracy, but performance taken is unknown. We evaluated the generalisation of three lightweight YOLO detectors (YOLOv5-nano, YOLOv5-small, YOLOv7-tiny), previously images, for detecting ~ 1,300 flower-visiting arthropod individuals nearly 24,000 captured a fixed smartphone setup. These field featured unseen backgrounds smaller arthropods than training data. model highest number trainable parameters, performed best, localising 91.21% Hymenoptera 80.69% Diptera individuals. However, classification recall was lower (80.45% 66.90%, respectively), partly due Syrphidae mimicking challenge smaller, blurrier flower visitors. This study reveals both potential limitations such models real-world automated monitoring, suggesting they work well larger sharply visible pollinators need improvement less sharp cases.

Language: Английский

Citations

0
Towards edge processing of images from insect camera traps DOI Creative Commons
Kim Bjerge, Henrik Karstoft, Toke T. Høye

et al.

Remote Sensing in Ecology and Conservation, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Abstract Insects represent nearly half of all known multicellular species, but knowledge about them lags behind for most vertebrate species. In part this reason, they are often neglected in biodiversity conservation policies and practice. Computer vision tools, such as insect camera traps, automated monitoring have the potential to revolutionize study conservation. To further advance trapping analysis their image data, effective processing pipelines needed. paper, we present a flexible fast pipeline designed analyse these recordings by detecting, tracking classifying nocturnal insects broad taxonomy 15 classes resolution individual moth A classifier with anomaly detection is proposed filter dark, blurred or partially visible that will be uncertain classify correctly. simple track‐by‐detection algorithm track classified incorporating feature embeddings, distance area cost. We evaluated computational speed power performance different edge computing devices (Raspberry Pi's NVIDIA Jetson Nano) compared various time‐lapse (TL) strategies tracking. The minimum difference detections was found 2‐min TL intervals 0.5 frames per second; however, fewer than one night, Pearson correlation decreases. Shifting from would reduce number recorded images allow real‐time on trap Raspberry Pi. Nano energy‐efficient solution, capable at fps. Our applied more 5.7 million second 12 light traps during two full seasons located diverse habitats, including bogs, heaths forests. results thus show scalability traps.

Language: Английский

Citations

0
Flower Visitation through the Lens: Exploring the Foraging Behaviour of Bombus terrestris with a Computer Vision-Based Application DOI Creative Commons
Zsófia Varga‐Szilay, Gergely Szövényi, Gábor Pozsgai

et al.

Insects, Journal Year: 2024, Volume and Issue: 15(9), P. 729 - 729

Published: Sept. 22, 2024

To understand the processes behind pollinator declines and for conservation of pollination services, we need to fundamental drivers influencing behaviour. Here, aimed elucidate how wild bumblebees interact with three plant species investigated their foraging behaviour varying flower densities. We video-recorded Bombus terrestris in 60 × cm quadrats Lotus creticus, Persicaria capitata, Trifolium pratense urban areas Terceira (Azores, Portugal). For automated bumblebee detection counting, created deep learning-based computer vision models custom datasets. achieved high model accuracy 0.88 0.95 Trifolium, indicating accurate detection. In our study, cover was only factor that influenced attractiveness patches, did not have an effect. detected a significant positive effect on patches flower-visiting bumblebees. The time spent per unit inflorescence surface area longer than those Persicaria. However, result indicate differences inflorescences among species. also justify vision-based analysis as reliable tool studying behavioural ecology.

Language: Английский

Citations

1