Early warning of Noctiluca scintillans blooms using in-situ plankton imaging system: An example from Dapeng Bay, P.R. China

Song, Junting; Bi, Hongsheng; Cai, Zhonghua; Cheng, Xuemin; He, Yonghong; Benfield, Mark C.; Fan, Chunlei

doi:10.1016/j.ecolind.2020.106123

Cited by 27 publications

(17 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, their large data bandwidths and power requirements generally limit them to cabled, moored observatories (Boss et al, 2022). More affordable modular plankton imaging technology (PlanktonScope) with low power requirements is now available, making its deployment on multiple platforms a possibility (Pollina et al, 2020;Song et al, 2020) Strategies that have been developed to improve sensor performance include using fluorescence lifetime-based sensing for O 2 , mechanical wipers for bio-optical measurements, and renewable reagents for CO 2 and pH sensors. It would be ideal if more analytes could be detected via time-resolved fluorescence like that available using O 2 optodes.…”

Section: Sensor Technology and Arctic Deployment Strategiesmentioning

confidence: 99%

Emerging Technologies and Approaches for In Situ, Autonomous Observing in the Arctic

Lee¹,

DeGrandpre²,

Guthrie³

et al. 2022

Oceanog

View full text Add to dashboard Cite

Understanding and predicting Arctic change and its impacts on global climate requires broad, sustained observations of the atmosphere-ice-ocean system, yet technological and logistical challenges severely restrict the temporal and spatial scope of observing efforts. Satellite remote sensing provides unprecedented, pan-Arctic measurements of the surface, but complementary in situ observations are required to complete the picture. Over the past few decades, a diverse range of autonomous platforms have been developed to make broad, sustained observations of the ice-free ocean, often with near-real-time data delivery. Though these technologies are well suited to the difficult environmental conditions and remote logistics that complicate Arctic observing, they face a suite of additional challenges, such as limited access to satellite services that make geolocation and communication possible. This paper reviews new platform and sensor developments, adaptations of mature technologies, and approaches for their use, placed within the framework of Arctic Ocean observing needs.

show abstract

Section: Sensor Technology and Arctic Deployment Strategiesmentioning

confidence: 99%

Emerging Technologies and Approaches for In Situ, Autonomous Observing in the Arctic

Lee¹,

DeGrandpre²,

Guthrie³

et al. 2022

Oceanog

View full text Add to dashboard Cite

show abstract

“…The scene-specific model starts with a scene classification model followed by a dedicated model for each scene for recognition. We provide the metrics for each approach using underwater plankton images from coastal waters collected by PlanktonScope [25, 26]. We then expand the comparison to test for general applicability using images collected by different underwater imaging systems.…”

Section: Introductionmentioning

confidence: 99%

Taming the data deluge: a novel end-to-end deep learning system for classifying marine biological and environmental images

Cheng

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Marine underwater imaging facilitates non-destructive sampling of species at frequencies, durations, and accuracies that are unattainable by conventional sampling methods. These systems necessitate complex automated processes to identify organisms efficiently, however, current frameworks struggle to disentangle ecological foreground components from their dispensable background content. Underwater image processing relies on common architecture: namely image binarization for segmenting potential targets, prior to information extraction and classification by deep learning models. While intuitive, this infrastructure underperforms as it has difficulty in handling: high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and target sizes that vary by several orders of magnitude. To overcome these issues, a new framework is presented that begins with a scene classifier to capture large within-image variation, such as disparities in particle concentration and dominant taxa. Following scene classification, scene-specific regional convolutional neural network (Mask R-CNN) models were trained to separate target objects into different taxonomic groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Usingin situcoastal PlanktonScope images, we compared the scene-specific models to the Mask R-CNN model including all scene categories without scene classification, defined as the full model, and found that the scene-specific approach outperformed the full model with >20% accuracy in noisy images. The full model missed up to 78% of the dominant taxonomic groups, such asLyngbya, Noctiluca, andPhaeocystiscolonies. This performance improvement is due to the scene classifier, which reduces the variation among images and allows an improved match between the observed taxonomic groups and the taxonomic groups in pre-trained models. We further tested the framework on images from a benthic video camera and an imaging sonar system. Results demonstrate that the procedure is applicable to different types of underwater images and achieves significantly more accurate results than the full model. Given that the unified framework is neither instrument nor ecosystem-specific, the proposed model facilitates deployment throughout the marine biome.

show abstract

“…Fast and efficient segmentation for a large number of in situ underwater plankton images can expedite the identification and enumeration of plankton species. Accurate identification and enumeration can facilitate understanding of the spatial and temporal dynamics of marine plankton and increase our capabilities to monitor and forecast the presence and outbreak of harmful marine species [3,4].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Downscaling Segmentation for Noisy, Low-Contrast in Situ Underwater Plankton Images

Cheng

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Finding and segmenting objects in noisy low-contrast in situ underwater plankton images is challenging because of the difficulty of separating potential plankton objects from the complex background and numerous and diverse other particles. In the present study, a dynamic downscaling model was developed to rapidly extract complete and clean regions of interest (ROIs) from images with highly variable content and quality. The original image was downscaled, and dynamic segmentation was performed in a scale pyramid space to ensure the integrity of weak targets based on local two-dimensional (2D) entropy parameters. Subsequently, a series of local thresholds and clustering gradients was examined iteratively for ROI selection. The performance of the local 2D entropy parameters relative to water turbidity (in a scattering medium and with high background noise) and image size was examined. To suppress the background and increase the sharpness of potential targets, a sharpness descriptor and gradient clustering were employed. The method was compared with the currently commonly used local threshold-based Sauvola segmentation using the same set of images. The results showed that the proposed method improves the ROI extraction accuracy and reduces oversegmentation for in situ underwater plankton images. It was concluded that the proposed method is a fast and robust segmentation technique and could facilitate the deployment of in situ plankton imaging systems for process-based research and routine plankton monitoring. INDEX TERMS dynamic downscaling, gradient clustering, in situ underwater plankton image, region of interest, scale pyramid space, segmentation.

show abstract

Early warning of Noctiluca scintillans blooms using in-situ plankton imaging system: An example from Dapeng Bay, P.R. China

Cited by 27 publications

References 36 publications

Emerging Technologies and Approaches for In Situ, Autonomous Observing in the Arctic

Emerging Technologies and Approaches for In Situ, Autonomous Observing in the Arctic

Taming the data deluge: a novel end-to-end deep learning system for classifying marine biological and environmental images

Dynamic Downscaling Segmentation for Noisy, Low-Contrast in Situ Underwater Plankton Images

Contact Info

Product

Resources

About