Introducing ARTiMiS: A low-cost flow imaging microscope for phytoplankton monitoring in engineered and natural environments
Open accessAbstract
Manual microscopy is the gold standard for phytoplankton monitoring in diverse engineered and natural environments. However, it is both labor-intensive and requires specialized training for accuracy and consistency, and therefore difficult to implement on a routine basis without significant time investment. Automation can reduce this burden by simplifying the measurement to a single indicator (e.g., chlorophyll fluorescence) measurable by a probe, or by processing samples on an automated cytometer for more granular information. The cost of commercially available flow imaging cytometers, however, poses a steep financial barrier to adoption. To overcome these labor and cost barriers, we developed ARTiMiS: the Autonomous Real-Time Microbial ‘Scope. The ARTiMiS is a low-cost flow imaging microscopy-based platform with onboard software capable of providing species-level quantitation of phytoplankton communities in real-time. ARTiMiS leverages novel multi-modal imaging and onboard machine learning-based data processing that is currently optimized for a curated and expandable database of industrially relevant microalgae. We demonstrate its operational limits, performance in identification of laboratory-cultivated microalgae, and potential for continuous monitoring of complex microalgal communities in full-scale cultivation systems. Synopsis: We introduce a platform for low-cost real-time imaging monitoring of phytoplankton and demonstrate its utility in real-time monitoring of laboratory- and full-scale microalgal cultivation systems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Devices such as the Raspberry Pi Camera Module V2 based on the Sony IMX219 sensor enable high-resolution analysis of morphological and pigmentary changes in individual microalgae colonies.
“Devices such as the Raspberry Pi Camera Module V2 (Pencoed, UK), which is based on the Sony IMX219 sensor (Atsugi, Japan), enable high-resolution analysis of morphological and pigmentary changes in individual microalgae colonies [254].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [254]
The Design and Construction section identifies an 8MP Raspberry Pi Camera V2 with a Sony IMX219 sensor, and the microalgal-classification section describes using images to distinguish cell morphologies. The paper does not report analysis of pigmentary changes or changes in individual microalgae colonies; its time-series analysis concerns community composition and biomass. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works
Limit of blank, limit of detection and limit of quantitation.
Armbruster, D., & Pry, T. (2008) · PubMed Central · doi:10.1111/aji.13807 · Cited by 2,092 · see citing works
Khan, M. I., Shin, J. H., & Kim, J. D. (2018) · Microbial Cell Factories · doi:10.1186/s12934-018-0879-x · Cited by 2,082 · see citing works
Decision and detection limits for calibration curves
Hubaux, A., & Vos, G. (1970) · Analytical Chemistry · doi:10.1021/ac60290a013 · Cited by 520 · see citing works
[11] Measurement of growth and viability of cells in culture
Patterson, M. K. (1979) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(79)58132-4 · Cited by 514 · see citing works
Daneshvar, E., Wicker, R. J., Show, P.-L., & Bhatnagar, A. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130884 · Cited by 493 · see citing works
A submersible imaging‐in‐flow instrument to analyze nano‐and microplankton: Imaging FlowCytobot
Olson, R. J., & Sosik, H. M. (2007) · Limnology and Oceanography Methods · doi:10.4319/lom.2007.5.195 · Cited by 480 · see citing works
An imaging-in-flow system for automated analysis of marine microplankton
Sieracki, C. K., Sieracki, M. E., & Yentsch, C. S. (1998) · Marine Ecology Progress Series · doi:10.3354/meps168285 · Cited by 438 · see citing works
Interactions of microalgae-bacteria consortia for nutrient removal from wastewater: A review
Fallahi, A., Rezvani, F., Asgharnejad, H., Nazloo, E. K., Hajinajaf, N., & Higgins, B. T. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.129878 · Cited by 409 · see citing works
Performance evaluation of object detection algorithms for video surveillance
Nascimento, J. C., & Marques, J. S. (2006) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2006.876287 · Cited by 284 · see citing works
Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case
Nowozin, S. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.77 · Cited by 163 · see citing works
Resource recovery from industrial effluents through the cultivation of microalgae: A review
., S. M. A., Peter, A. P., Chew, K. W., Munawaroh, H. S. H., & Show, P. L. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.125461 · Cited by 125 · see citing works
Microalgae classification based on machine learning techniques
Otálora, P., Guzmán, J. L., Acién, F. G., Berenguel, M., & Reul, A. (2021) · Algal Research · doi:10.1016/j.algal.2021.102256 · Cited by 111 · see citing works
LudusScope: Accessible Interactive Smartphone Microscopy for Life-Science Education
Kim, H., Gerber, L. C., Chiu, D., Lee, S. A., Cira, N., Xia, S. Y., & Riedel-Kruse, I. H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162602 · Cited by 83 · see citing works
Machine learning techniques to characterize functional traits of plankton from image data
Orenstein, E., Ayata, S., Maps, F., Becker, E., Benedetti, F., Biard, T., de Garidel-Thoron, T., Ellen, J. S., Ferrario, F., Giering, S., et al. (2022) · Limnology and Oceanography · doi:10.1002/lno.12101 · Cited by 80 · see citing works
PlanktoScope: Affordable Modular Quantitative Imaging Platform for Citizen Oceanography
Pollina, T., Larson, A. G., Lombard, F., et al. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.949428 · Cited by 72 · see citing works
Routine Management of Microalgae Using Autofluorescence from Chlorophyll
Takahashi, T. (2019) · Molecules · doi:10.3390/molecules24244441 · Cited by 67 · see citing works
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Bhalodia, R., Elhabian, S., Kavan, L., & Whitaker, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04747-4_23 · Cited by 62 · see citing works
MorphoCluster: Efficient Annotation of Plankton Images by Clustering
Schröder, S., Kiko, R., & Koch, R. (2020) · Sensors · doi:10.3390/s20113060 · Cited by 59 · see citing works
Camoying, M. G., & Yñiguez, A. T. (2016) · Limnology and Oceanography Methods · doi:10.1002/lom3.10090 · Cited by 56 · see citing works
Zhou, L., Li, Q., Huo, G., & Zhou, Y. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/3792805 · Cited by 50 · see citing works
Monitoring of Microalgal Processes
Havlik, I., Scheper, T. H., & Reardon, K. F. (2015) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2015_328 · Cited by 49 · see citing works
Hardison, D. R., Holland, W. C., Currier, R. D., Kirkpatrick, B., Stumpf, R. P., Fanara, T., Burris, D., Reich, A., Kirkpatrick, G. J., & Litaker, R. W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218489 · Cited by 45 · see citing works
Li, H., Soto-Montoya, H., Voisin, M., Valenzuela, L. F., & Prakash, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/684423 · Cited by 44 · see citing works
Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery
Kerr, T., Clark, J. R., Fileman, E. S., Widdicombe, C. E., & Pugeault, N. (2020) · IEEE Access · doi:10.1109/access.2020.3022242 · Cited by 44 · see citing works
Daniels, A. L., Calderon, C. P., & Randolph, T. W. (2020) · Biotechnology and Bioengineering · doi:10.1002/bit.27501 · Cited by 42 · see citing works
Sensor-based detection of algal blooms for public health advisories and long-term monitoring
Rome, M. B., Beighley, R. E., & Faber, T. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.144984 · Cited by 39 · see citing works
AlMomani, F. A., & Örmeci, B. (2018) · Environmental Monitoring and Assessment · doi:10.1007/s10661-018-6468-y · Cited by 36 · see citing works
Robotic microscopy for everyone: the OpenFlexure MicroscopePreprint
Collins, J. T., Knapper, J., Stirling, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/861856 · Cited by 24 · see citing works
Flat-Field and Colour Correction for the Raspberry Pi Camera Module
Bowman, R. W., Vodenicharski, B., Collins, J. T., et al. (2020) · Journal of Open Hardware · doi:10.5334/joh.20 · Cited by 22 · see citing works
Publication practice in Taxonomy: Global inequalities and potential bias against negative results
Salvador, R. B., Cavallari, D. C., Rands, D., & Tomotani, B. M. (2022) · PLOS ONE · doi:10.1371/journal.pone.0269246 · Cited by 21 · see citing works
Boss, E., Waite, A. M., Karstensen, J., et al. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.929436 · Cited by 15 · see citing works
Hüller, R., Glossner, E., Schaub, S., et al. (1994) · Cytometry · doi:10.1002/cyto.990170202 · Cited by 13 · see citing works
SAMSON: Spectral Absorption-fluorescence Microscopy System for ON-site-imaging of algae
Deglint, J., Tang, L., Wang, Y., et al. (2018) · Journal of Computational Vision and Imaging Systems · doi:10.15353/jcvis.v4i1.324 · Cited by 9 · see citing works
Alam, M. M., Hodaei, M., Hartnett, E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.23.576871 · Cited by 6 · see citing works
Self-citationMolitor, H. R., Kim, G.-Y., Hartnett, E., et al. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-7c4s5 · Cited by 4 · see citing works
Self-citationIrisson, J., Salinas, L., Colin, S., et al. (2022) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dębowski, M., Kazimierowicz, J., & Zieliński, M. (2025) · Applied Sciences · doi:10.3390/app15137324 · Cited by 30 · see citing works
- Authors
- Gincley, B., Khan, F., Hartnett, E., Fisher, A., & Pinto, A. J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Pinto, Ameet J.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2024-03-03
- OpenAlex
- W4392343331
- Cited by
- 2
- References
- 39
- Citations by year
- 2025: 1 · 2024: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- National Science Foundation (2220792)
- U.S. Department of Energy (DEEE0009270)
- Office of Energy Efficiency and Renewable Energy (DE-EE0009270)
- Division of Chemical, Bioengineering, Environmental, and Transport Systems (2220792)
- Office of Energy Efficiency
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- flow imaging microscopy, phytoplankton monitoring, low-cost instrumentation, real-time monitoring, onboard machine learning, multimodal imaging, automated microscopy, chlorophyll fluorescence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
