Trends in digital image processing of isolated microalgae by incorporating classification algorithm
Abstract
Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
The selection of robust strains that could harvest a high amount of desired products is a crucial step in the large-scale production of biofuels.
“Hence, the selection of robust strains that could harvest a high amount of desired products is a crucial step in the large-scale production of biofuels [29]”
From Microalgae biofuels: illuminating the path to a sustainable future amidst challenges and opportunities (2024) · cites this paper as [29]
Cites
Biological identifications through DNA barcodes
Hebert, P. D. N., Cywinska, A., Ball, S. L., & deWaard, J. R. (2003) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.2002.2218 · Cited by 13,376 · see citing works
A review of feature selection methods with applications
Jović, A., Brkić, K., & Bogunovic, N. (2015) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2015.7160458 · Cited by 1,238 · see citing works
Imaging modes of atomic force microscopy for application in molecular and cell biology
Dufrêne, Y. F., Ando, T., Garcia, R. A., Alsteens, D., Martínez-Martín, D., Engel, A. H., Gerber, C., & Müller, D. J. (2017) · Nature Nanotechnology · doi:10.1038/nnano.2017.45 · Cited by 952 · see citing works
Advantages and limitations of quantitative PCR (Q-PCR)-based approaches in microbial ecology
Smith, C. J., & Osborn, A. M. (2008) · FEMS Microbiology Ecology · doi:10.1111/j.1574-6941.2008.00629.x · Cited by 939 · see citing works
Encyclopedia of analytical science
(2005) · Choice Reviews Online · doi:10.5860/choice.43-0032 · Cited by 916 · see citing works
Microalgae: A potential alternative to health supplementation for humans
Koyande, A. K., Chew, K. W., Rambabu, K., Tao, Y., Chu, D.-T., & Show, P.-L. (2019) · Food Science and Human Wellness · doi:10.1016/j.fshw.2019.03.001 · Cited by 908 · see citing works
Self-citationSuganya, T., Varman, M., Masjuki, H. H., & Renganathan, S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.026 · Cited by 793 · see citing works
Support vector machine applications in the field of hydrology: A review
Naganna, S. R., & Deka, P. C. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.02.002 · Cited by 780 · see citing works
Microalgae for High-Value Products Towards Human Health and Nutrition
Barkia, I., Saari, N., & Manning, S. R. (2019) · Marine Drugs · doi:10.3390/md17050304 · Cited by 719 · see citing works
Garibyan, L., & Avashia, N. (2013) · Journal of Investigative Dermatology · doi:10.1038/jid.2013.1 · Cited by 691 · see citing works
Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A., & Bubała, K. (2014) · Bioresource Technology · doi:10.1016/j.biortech.2014.01.021 · Cited by 667 · see citing works
AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions
Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021) · SN Computer Science · doi:10.1007/s42979-021-00557-0 · Cited by 619 · see citing works
Intelligent Image-Activated Cell Sorting
Nitta, N., Sugimura, T., Isozaki, A., Mikami, H., Hiraki, K., Sakuma, S., Iino, T., Arai, F., Endo, T., Fujiwaki, Y., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.028 · Cited by 544 · see citing works
Imaging without lenses: achievements and remaining challenges of wide-field on-chip microscopy
Greenbaum, A., Luo, W., Su, T., Göröcs, Z. S., Xue, L., Isikman, S. O., Coskun, A. F., Mudanyali, O., & Özcan, A. (2012) · Nature Methods · doi:10.1038/nmeth.2114 · Cited by 542 · see citing works
Sustainability of the four generations of biofuels – A review
Mat Aron, N. S., Khoo, K. S., Chew, K. W., Show, P. L., Chen, W., & Nguyen, T. H. P. (2020) · International Journal of Energy Research · doi:10.1002/er.5557 · Cited by 479 · see citing works
Self-citationHandbook of Toxicology of Chemical Warfare Agents
Gupta, R. C. (2015) · Elsevier eBooks · doi:10.1016/c2013-0-15402-5 · Cited by 475 · see citing works
Handbook of Medical Image Processing and Analysis
Bankman, I. N. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-373904-9.x0001-4 · Cited by 453 · see citing works
(2020) · Elsevier eBooks · doi:10.1016/c2018-0-05339-6 · Cited by 398 · see citing works
(2018) · Elsevier eBooks · doi:10.1016/c2015-1-01045-6 · Cited by 396 · see citing works
Potential utilization of bioproducts from microalgae for the quality enhancement of natural products
Tang, D. Y. Y., Khoo, K. S., Chew, K. W., Tao, Y., Ho, S., & Show, P. L. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.122997 · Cited by 393 · see citing works
Self-citationColony formation in the cyanobacterium Microcystis
Xiao, M., Li, M., & REYNOLDS, C. S. (2018) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12401 · Cited by 391 · see citing works
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection
Aslan, M. F., Ünlerşen, M. F., Sabancı, K., & Durdu, A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106912 · Cited by 355 · see citing works
DNA barcodes for 1/1000 of the animal kingdom
Hebert, P. D. N., deWaard, J. R., & Landry, J. (2009) · Biology Letters · doi:10.1098/rsbl.2009.0848 · Cited by 336 · see citing works
Recent advances in downstream processing of microalgae lipid recovery for biofuel production
Khoo, K. S., Chew, K. W., Yew, G. Y., Leong, W. H., Chai, Y. H., Show, P. L., & Chen, W.-H. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.122996 · Cited by 315 · see citing works
Self-citationValuable bioproducts obtained from microalgal biomass and their commercial applications: A review
Loganathan, B. G., Orsat, V., & Mark, L. (2018) · Environmental Engineering Research · doi:10.4491/eer.2017.220 · Cited by 253 · see citing works
Materials Characterization Using Nondestructive Evaluation (NDE) Methods
Hübschen, G., Altpeter, I., Herrmann, H.-G., et al. (2016) · Elsevier eBooks · doi:10.1016/c2014-0-00661-2 · Cited by 234 · see citing works
Microalgal-based feed: Promising alternative feedstocks for livestock and poultry production
Saadaoui, I., Rasheed, R., Aguilar, A., Cherif, M., Al Jabri, H., Sayadi, S., & Manning, S. R. (2021) · Journal of Animal Science and Biotechnology · doi:10.1186/s40104-021-00593-z · Cited by 223 · see citing works
Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering
Suzuki, Y., Kobayashi, K., Wakisaka, Y., Deng, D., Tanaka, S., Huang, C., Lei, C., Sun, C., Liu, H., Fujiwaki, Y., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902322116 · Cited by 215 · see citing works
Machine learning and applications in microbiology
Goodswen, S. J., Barratt, J., Kennedy, P. J., Kaufer, A., Calarco, L., & Ellis, J. (2021) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuab015 · Cited by 213 · see citing works
Li, X., Dreher, T. W., & Li, R. (2016) · Harmful Algae · doi:10.1016/j.hal.2015.10.015 · Cited by 196 · see citing works
High-Resolution Electron Microscopy
Spence, J. C. H. (2013) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199668632.001.0001 · Cited by 188 · see citing works
High-throughput imaging flow cytometry by optofluidic time-stretch microscopy
Lei, C., Kobayashi, H., Wu, Y., Li, M., Isozaki, A., Yasumoto, A., Mikami, H., Ito, T., Nitta, N., Sugimura, T., et al. (2018) · Nature Protocols · doi:10.1038/s41596-018-0008-7 · Cited by 170 · see citing works
Intelligent food processing: Journey from artificial neural network to deep learning
Nayak, J., Vakula, K., Dinesh, P., et al. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100297 · Cited by 168 · see citing works
Mutanda, T., Naidoo, D., Bwapwa, J. K., & Anandraj, A. (2020) · Frontiers in Energy Research · doi:10.3389/fenrg.2020.598803 · Cited by 160 · see citing works
Rani, P., Kotwal, S., Manhas, J., Sharma, V., & Sharma, S. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09639-x · Cited by 155 · see citing works
Mechanism behind autoflocculation of unicellular green microalgae Ettlia texensis
Salim, S., Kosterink, N. R., Wacka, N. D. T., Vermuë, M. H., & Wijffels, R. H. (2014) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2014.01.026 · Cited by 149 · see citing works
Biomass, Biopolymer-Based Materials, and Bioenergy
(2019) · Elsevier eBooks · doi:10.1016/c2017-0-00839-x · Cited by 146 · see citing works
Machine Learning Algorithm Validation
Maleki, F., Muthukrishnan, N., Ovens, K. L., Reinhold, C., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.004 · Cited by 144 · see citing works
Microscopy refocusing and dark-field imaging by using a simple LED array
Zheng, G., Kolner, C., & Yang, C. (2011) · Optics Letters · doi:10.1364/ol.36.003987 · Cited by 142 · see citing works
Yap, B., Crawford, S., Dagastine, R. R., Scales, P. J., & Martin, G. J. O. (2016) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-016-1848-1 · Cited by 139 · see citing works
How does the Internet of Things (IoT) help in microalgae biorefinery?
Wang, K., Khoo, K. S., Leong, H. Y., Nagarajan, D., Chew, K. W., Ting, H. Y., Selvarajoo, A., Chang, J., & Show, P. L. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107819 · Cited by 134 · see citing works
Self-citationLatest developments in wastewater treatment and biopolymer production by microalgae
Lutzu, G. A., Ciurli, A., Chiellini, C., Caprio, F. D., Concas, A., & Dunford, N. T. (2020) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2020.104926 · Cited by 131 · see citing works
Biodegradation of low-density polyethylene (LDPE) sheet by microalga, Uronema africanum Borge
Sanniyasi, E., Gopal, R. K., Gunasekar, D. K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-96315-6 · Cited by 129 · see citing works
Eiler, A., Drakare, S., Bertilsson, S., Pernthaler, J., Peura, S., Rofner, C., Šimek, K., Yang, Y., Znachor, P., & Lindström, E. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053516 · Cited by 123 · see citing works
Imaging flow cytometry for phytoplankton analysis
Dashkova, V., Malashenkov, D. V., Poulton, N. J., Vorobjev, I. A., & Barteneva, N. S. (2016) · Methods · doi:10.1016/j.ymeth.2016.05.007 · Cited by 120 · 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
Polymer Science and Nanotechnology
(2020) · Elsevier eBooks · doi:10.1016/c2018-0-01134-2 · Cited by 110 · see citing works
Nanomaterials for Solar Cell Applications
Sakho, E. H. M., Wu, J., Kalarikkal, N. 1., et al. (2019) · Elsevier eBooks · doi:10.1016/c2016-0-03432-0 · Cited by 109 · see citing works
Progress on microalgae cultivation in wastewater for bioremediation and circular bioeconomy
Satya, A. D. M., Cheah, W. Y., Yazdi, S. K., Cheng, Y., Khoo, K. S., Vo, D. N., Bui, X. D., Vithanage, M. S., & Show, P. L. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114948 · Cited by 105 · see citing works
Self-citationApplication of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology
Al-Mahasneh, M. A., Aljarrah, M., Rababah, T., et al. (2016) · Food Engineering Reviews · doi:10.1007/s12393-016-9141-7 · Cited by 103 · see citing works
Xu, Z., Jiang, Y., Ji, J., Forsberg, E., Li, Y., & He, S. (2020) · Optics Express · doi:10.1364/oe.406036 · Cited by 101 · see citing works
Advancement of renewable energy technologies via artificial and microalgae photosynthesis
Xie, Y., Khoo, K. S., Chew, K. W., Devadas, V. V., Phang, S. J., Lim, H. R., Rajendran, S., & Show, P. L. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127830 · Cited by 100 · see citing works
Self-citationSönmez, M. E., Eczacıoğlu, N., Gümüş, N. E., Aslan, M. F., Sabancı, K., & Aşıkkutlu, B. (2021) · Algal Research · doi:10.1016/j.algal.2021.102568 · Cited by 96 · see citing works
Chong, J. W. R., Yew, G. Y., Khoo, K. S., Ho, S.-H., & Show, P. L. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.112782 · Cited by 95 · see citing works
Self-citationKatırcıoğlu, H., Aslım, B., Türker, A. R., et al. (2007) · Bioresource Technology · doi:10.1016/j.biortech.2007.08.068 · Cited by 90 · see citing works
Zou, S., Fei, C., Wang, C., Gao, Z., Bao, Y., He, M., & Wang, C. (2016) · Scientific Reports · doi:10.1038/srep36822 · Cited by 87 · see citing works
A Beginner's Guide to Image Preprocessing Techniques
Chaki, J., & Dey, N. (2018) · CRC Press eBooks · doi:10.1201/9780429441134 · Cited by 87 · see citing works
Azaman, S. N. A., Nagao, N., Yusoff, F. M., Tan, S. W., & Yeap, S. K. (2017) · PeerJ · doi:10.7717/peerj.3473 · Cited by 86 · see citing works
Force and energy requirement for microalgal cell disruption: An atomic force microscope evaluation
Lee, A. K., Lewis, D. M., & Ashman, P. J. (2012) · Bioresource Technology · doi:10.1016/j.biortech.2012.10.032 · Cited by 83 · see citing works
Environmentally friendly polymer nanocomposites
Ray, S. S. (2013) · Woodhead Publishing Limited eBooks · doi:10.1533/9780857097828 · Cited by 82 · see citing works
Guo, L., Sui, Z., Zhang, S., et al. (2015) · INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY · doi:10.1099/ijs.0.000076 · Cited by 82 · see citing works
Eutrophication reduces the nutritional value of phytoplankton in boreal lakes
Taipale, S. J., Vuorio, K. M., Aalto, S. L., et al. (2019) · Environmental Research · doi:10.1016/j.envres.2019.108836 · Cited by 82 · see citing works
Mamo, T. T., & Mekonnen, Y. S. (2019) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-019-03149-0 · Cited by 81 · see citing works
Hazeem, L. J., Yeşilay, G., Dewailly, E., et al. (2019) · Nanomaterials · doi:10.3390/nano9070914 · Cited by 80 · see citing works
A preliminary study on automated freshwater algae recognition and classification system
Mosleh, M. A. A., Manssor, H., Malek, S., Milow, P., & Salleh, A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s17-s25 · Cited by 77 · see citing works
Identification and enumeration of cyanobacteria species using a deep neural network
Baek, S., Pyo, J., Pachepsky, Y. A., et al. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106395 · Cited by 77 · see citing works
Enhancement of lipid production from algal biomass through various growth parameters
Sharma, J., Kumar, S. S., Bishnoi, N. R., & Pugazhendhi, A. (2018) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2018.08.103 · Cited by 75 · see citing works
Continuous production of carotenoids from Dunaliella salina
Kleinegris, D. M. M., Janssen, M., Brandenburg, W. A., & Wijffels, R. H. (2011) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2010.11.005 · Cited by 73 · see citing works
Evaluation of DNA extraction methods for freshwater eukaryotic microalgae
Eland, L. E., Davenport, R. J., & Mota, C. R. (2012) · Water Research · doi:10.1016/j.watres.2012.07.023 · Cited by 73 · see citing works
Maheshan, C. M., & Kumar, H. P. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-1800-x · Cited by 73 · see citing works
Yun, H.-S., Kim, Y.-S., & Yoon, H. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04447 · Cited by 73 · see citing works
Zambrano, J., García-Encina, P. A., Hernández, F. J., et al. (2021) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2021.102226 · Cited by 67 · see citing works
Douglass, M. J. J. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00913-z · Cited by 66 · see citing works
Godhe, A., Anderson, D. M., & Rehnstam‐Holm, A. (2002) · Harmful Algae · doi:10.1016/s1568-9883(02)00049-5 · Cited by 62 · see citing works
Soft Computing Based Medical Image Analysis
(2018) · Elsevier eBooks · doi:10.1016/c2016-0-04419-4 · Cited by 62 · see citing works
Deep learning-based ResNeXt model in phycological studies for future
Yadav, D. P., Jalal, A. S., Garlapati, D., Hossain, K., Goyal, A., & Pant, G. (2020) · Algal Research · doi:10.1016/j.algal.2020.102018 · Cited by 61 · see citing works
Coltelli, P., Barsanti, L. A., Evangelista, V., et al. (2014) · Environmental Science Processes & Impacts · doi:10.1039/c4em00451e · Cited by 60 · see citing works
Automated Microalgae Image Classification
Promdaen, S., Wattuya, P., & Sanevas, N. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.05.182 · Cited by 60 · see citing works
Khalil, S., Mahnashi, M. H., Hussain, M., et al. (2021) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2021.06.004 · Cited by 60 · see citing works
Costa, P. R., Robertson, A., & Quilliam, M. A. (2015) · Marine Drugs · doi:10.3390/md13042046 · Cited by 59 · see citing works
High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton
Lai, Q. T. K., Lee, K. C. M., Tang, A. H. L., et al. (2016) · Optics Express · doi:10.1364/oe.24.028170 · Cited by 59 · see citing works
Park, J., Lee, H., Park, C. Y., Hasan, S., Heo, T., & Lee, W. H. (2019) · Water · doi:10.3390/w11071338 · Cited by 59 · see citing works
Pattern Recognition and Signal Analysis in Medical Imaging
(2014) · Elsevier eBooks · doi:10.1016/b978-0-12-409545-8.00014-5 · Cited by 59 · see citing works
Smart Farming through Responsible Leadership in Bangladesh: Possibilities, Opportunities, and Beyond
Haque, A., Islam, N., Samrat, N. H., Dey, S., & Ray, B. R. (2021) · Sustainability · doi:10.3390/su13084511 · Cited by 57 · see citing works
An, T., Winshell, J., Scorzetti, G., Fell, J. W., & Rein, K. S. (2009) · Toxicon · doi:10.1016/j.toxicon.2009.08.018 · Cited by 56 · see citing works
Forest Type Classification: A Hybrid NN-GA Model Based Approach
Chatterjee, S., Ghosh, S., Dawn, S., et al. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2757-1_23 · Cited by 55 · see citing works
Introduction to the Analysis and Risk of Nanomaterials in Environmental and Food Samples
Farré, M., & Barceló, . (2012) · Comprehensive analytical chemistry · doi:10.1016/b978-0-444-56328-6.00001-3 · Cited by 54 · see citing works
Texture Feature Extraction Techniques for Image Recognition
Chaki, J., & Dey, N. (2019) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-981-15-0853-0 · Cited by 54 · see citing works
Liu, M., Gu, H., Krock, B., et al. (2020) · Harmful Algae · doi:10.1016/j.hal.2020.101868 · Cited by 54 · see citing works
Water monitoring by means of digital microscopy identification and classification of microalgae
Barsanti, L. A., Birindelli, L., & Gualtieri, P. (2021) · Environmental Science Processes & Impacts · doi:10.1039/d1em00258a · Cited by 54 · see citing works
Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
(2019) · Elsevier eBooks · doi:10.1016/c2017-0-02827-6 · Cited by 53 · see citing works
Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
Drews, P., Colares, R. G., Machado, P., de Faria, M., Detoni, A. M. S., & Tavano, V. M. (2013) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-013-0121-y · Cited by 50 · see citing works
Automatic identification of Scenedesmus polymorphic microalgae from microscopic images
Giraldo-Zuluaga, J.-H., Salazar, A., Diez, G., Gomez-Villa, A., Martínez, T., Vargas, J. F., & Peñuela, M. (2017) · Pattern Analysis and Applications · doi:10.1007/s10044-017-0662-3 · Cited by 50 · see citing works
A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
Salido, J., Sánchez, C., Ruiz-Santaquiteria, J., et al. (2020) · Applied Sciences · doi:10.3390/app10176033 · Cited by 50 · see citing works
Jain, S., & Salau, A. O. (2019) · Cogent Engineering · doi:10.1080/23311916.2019.1599537 · Cited by 48 · see citing works
Capson‐Tojo, G., Torres-Aravena, Á., Muñoz, R., Bartáček, J., & Jeison, D. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.12.052 · Cited by 46 · see citing works
Wu, Z. W., Wu, Z. W., Jiang, S., et al. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.11.053 · Cited by 46 · see citing works
Lim, D. K. Y., Schuhmann, H., Thomas‐Hall, S. R., Chan, C. K., Wass, T. J., Aguilera, F., Adarme-Vega, T. C., Dal’Molin, C. G. D. O., Thorpe, G. J., Batley, J., et al. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.06.003 · Cited by 45 · see citing works
Potential applications of nuisance microalgae blooms
Kim, J. K., Kottuparambil, S., Moh, S. H., Lee, T. K., Kim, Y.-J., Rhee, J.-S., Choi, E.-M., Kim, B. H., Yu, Y. J., Yarish, C., et al. (2015) · Journal of Applied Phycology · doi:10.1007/s10811-014-0410-7 · Cited by 40 · see citing works
Image Acquisition and Techniques to Perform Image Acquisition
Mishra, V. K., Kumar, S., & Shukla, N. (2017) · SAMRIDDHI A Journal of Physical Sciences Engineering and Technology · doi:10.18090/samriddhi.v9i01.8333 · Cited by 40 · see citing works
Deglint, J., Jin, C., Chao, A., & Wong, A. M. (2018) · IEEE Access · doi:10.1109/access.2018.2889017 · Cited by 40 · see citing works
Automatic Identification of Algal Community from Microscopic Images
Santhi, N., Pradeepa, C., Subashini, P., & Senthil, K. (2013) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s12844 · Cited by 38 · see citing works
Intelligent Data Analysis for Biomedical Applications
(2019) · Elsevier eBooks · doi:10.1016/c2017-0-03676-5 · Cited by 37 · see citing works
Asghari, S., Rajabi, F., Tarrahi, R., Salehi-Lisar, S. Y., Asnaashari, S., Omidi, Y., & Movafeghi, A. (2019) · Journal of Applied Phycology · doi:10.1007/s10811-019-01921-7 · Cited by 36 · see citing works
Formosa‐Dague, C., Gernigon, V., Castelain, M., Daboussi, F., & Guiraud, P. (2018) · Algal Research · doi:10.1016/j.algal.2018.06.010 · Cited by 35 · see citing works
Song, J., Bi, H., Cai, Z., Cheng, X., He, Y., Benfield, M. C., & Fan, C. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106123 · Cited by 35 · see citing works
Bordenave, C. D., Muggia, L., Chiva, S., et al. (2021) · Algal Research · doi:10.1016/j.algal.2021.102561 · Cited by 34 · see citing works
Mchau, G., Makule, E. E., Machunda, R. L., et al. (2019) · Journal of Water and Health · doi:10.2166/wh.2019.083 · Cited by 33 · see citing works
Roncalli, V., Turner, J. T., Kulis, D. M., et al. (2015) · Harmful Algae · doi:10.1016/j.hal.2015.11.003 · Cited by 32 · see citing works
Region Growing for Segmenting Green Microalgae Images
Borges, V. R. P., de Oliveira, M. C. F., da Silva, T. G., et al. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2615606 · Cited by 32 · see citing works
Harmon, J., Mikami, H., Kanno, H., Ito, T., & Goda, K. (2020) · OSA Continuum · doi:10.1364/osac.387523 · Cited by 31 · see citing works
DNA barcoding approach to characterize microalgae isolated from freshwater systems in Ecuador
Ballesteros, I., Terán, P., Guamán-Burneo, C., González, N., Cruz, A., & Castillejo, P. (2021) · Neotropical Biodiversity · doi:10.1080/23766808.2021.1920296 · Cited by 31 · see citing works
Luo, J., Zhang, H., Forsberg, E., Hou, S., Li, S., Xu, Z., Chen, X., Sun, X., & He, S. (2021) · Optics Express · doi:10.1364/oe.438253 · Cited by 31 · see citing works
Sunoj, S., Hammed, A. M., Igathinathane, C., Eshkabilov, S. L., & Şimşek, H. (2021) · Algal Research · doi:10.1016/j.algal.2021.102487 · Cited by 30 · see citing works
Mythili, Y., Lopus, M., & Wilson, L. (2010) · Methods in cell biology · doi:10.1016/s0091-679x(10)95011-5 · Cited by 29 · see citing works
Detecting and Tracking Nonfluorescent Nanoparticle Probes in Live Cells
Wang, G., & Fang, N. (2012) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-391857-4.00004-5 · Cited by 29 · see citing works
The Role of Life Cycle Characteristics in Harmful Algal Bloom Dynamics
Azanza, R. V., Brosnahan, M. L., Anderson, D. M., et al. (2018) · Ecological studies · doi:10.1007/978-3-319-70069-4_8 · Cited by 29 · see citing works
Automatic Identification of Diatoms with Circular Shape using Texture Analysis
Luo, Q., Gao, Y., Luo, J., Chen, C., Liang, J., & Yang, C. (2011) · Journal of Software · doi:10.4304/jsw.6.3.428-435 · Cited by 28 · see citing works
Jirayupat, C., Nagashima, K., Hosomi, T., Takahashi, T., Tanaka, W., Samransuksamer, B., Zhang, G., Liu, J., Kanai, M., & Yanagida, T. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c03163 · Cited by 28 · see citing works
Techniques for characterizing the structure and properties of polymer nanocomposites
Ray, S. S. (2013) · Elsevier eBooks · doi:10.1533/9780857097828.1.74 · Cited by 27 · see citing works
Fluorescence-assisted image analysis of freshwater microalgae
Walker, R. F., Ishikawa, K., & Kumagai, M. (2002) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(02)00057-x · Cited by 25 · see citing works
Bioprospecting microalgae from natural algal bloom for sustainable biomass and biodiesel production
Pandey, M. K., Dasgupta, C. N., Mishra, S. K., Srivastava, M., Gupta, V. K., Suseela, M. R., & Ramteke, P. W. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-09856-2 · Cited by 25 · see citing works
Tsunoda, M., Isailović, D., & Yeung, E. S. (2008) · Journal of Microscopy · doi:10.1111/j.1365-2818.2008.02091.x · Cited by 24 · see citing works
Identification of microalgae by hyperspectral microscopic imaging system
Wei, L., Su, K., Zhu, S., et al. (2017) · Spectroscopy Letters · doi:10.1080/00387010.2017.1287094 · Cited by 24 · see citing works
Yang, Q., Jiang, Z., Huang, C.-H., et al. (2018) · Antonie van Leeuwenhoek · doi:10.1007/s10482-018-1074-0 · Cited by 24 · see citing works
Lutz, S., Procházková, L., Benning, L. G., et al. (2019) · Fottea · doi:10.5507/fot.2019.003 · Cited by 24 · see citing works
Alp, M. T., Özbay, Ö., & Sungur, M. A. (2012) · Ekoloji · doi:10.5053/ekoloji.2011.827 · Cited by 23 · see citing works
Bumbac, C., Manea, E. E., Banciu, A. R., et al. (2019) · Revista de Chimie · doi:10.37358/rc.19.1.6898 · Cited by 21 · see citing works
Huo, D., Chen, Y., Zheng, T., Liu, X., Zhang, X., Yu, G., Qiao, Z., & Li, R. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00971 · Cited by 20 · see citing works
Kalafi, E. Y., Town, C. P., & Dhillon, S. K. (2017) · Folia Morphologica · doi:10.5603/fm.a2017.0079 · Cited by 19 · see citing works
Machine Learning Algorithms Applied to Identify Microbial Species by Their Motility
Riekeles, M., Schirmack, J., & Schulze‐Makuch, D. (2021) · Life · doi:10.3390/life11010044 · Cited by 19 · see citing works
Abdulhussain, A. H., Cook, K. B., Turner, A. D., et al. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.652225 · Cited by 19 · see citing works
Robot Systems for Rail Transit Applications
(2020) · Elsevier eBooks · doi:10.1016/c2019-0-04615-8 · Cited by 19 · see citing works
Hernández-Becerril, D. U., Lau, W. L. S., Hii, K. S., et al. (2018) · Frontiers in Marine Science · doi:10.3389/fmars.2018.00366 · Cited by 18 · see citing works
Internet of Things in Biomedical Engineering
(2019) · Elsevier eBooks · doi:10.1016/c2018-0-02203-3 · Cited by 17 · see citing works
Iyer, G., Nagle, V. L., Gupte, Y. V., Desai, S., Iyer, M., Moramkar, N., & Sawant, V. (2015) · Cited by 13 · see citing works
Image Noise Preprocessing of Interactive Projection System Based on Switching Filtering Scheme
Yu, L. (2018) · Complexity · doi:10.1155/2018/1258306 · Cited by 13 · see citing works
A quick method for obtaining high-quality DNA barcodes without DNA extraction in microalgae
Fei, C., Zou, S., Wang, T., et al. (2020) · Journal of Applied Phycology · doi:10.1007/s10811-019-01926-2 · Cited by 13 · see citing works
Enhanced DNA extraction and PCR amplification of SSU ribosomal genes from crustose coralline algae
Vidal, R., Meneses, I., & Smith, M. (2002) · Journal of Applied Phycology · doi:10.1023/a:1019975409640 · Cited by 11 · see citing works
PCR identification of toxic euglenid species Euglena sanguinea
Kulczycka, A., Łukomska‐Kowalczyk, M., Zakryś, B., et al. (2018) · Journal of Applied Phycology · doi:10.1007/s10811-017-1376-z · Cited by 11 · see citing works
Real-time red tide algae recognition using SVM and SVDD
Jiang, T., Wang, C., Boliang, W., et al. (2010) · doi:10.1109/icicisys.2010.5658453 · Cited by 10 · see citing works
Iamsiri, S., Sanevas, N., Watcharopas, C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22750-0_12 · Cited by 10 · see citing works
A beginner’s guide to improving image acquisition in fluorescence microscopy
Ogama, T. (2020) · The Biochemist · doi:10.1042/bio20200075 · Cited by 10 · see citing works
Ciurli, A., Modeo, L., Pardossi, A., & Chiellini, C. (2021) · Algal Research · doi:10.1016/j.algal.2021.102202 · Cited by 8 · see citing works
Suresh, A., Ayele, A., & Benor, S. (2019) · Ethiopian Journal of Science and Sustainable Development · doi:10.20372/ejssdastu:v6.i2.2019.102 · Cited by 5 · see citing works
Yang, M., Wang, W., Gao, Q., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-764817/v1 · Cited by 3 · see citing works
Microalgae role in sustainable bioenergy generation as determined by light microscopy
Abbas, M., Shaheen, S., Pervaiz, M., Jaffer, M., & Tahir, A. (2022) · Microscopy Research and Technique · doi:10.1002/jemt.24042 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wang, M., Ye, X., Bi, H., & Shen, Z. (2024) · Biotechnology for Biofuels and Bioproducts · doi:10.1186/s13068-024-02461-0 · Cited by 201 · see citing works
Mafat, I. H., Palla, S., & Surya, D. V. (2024) · Value Added Products From Bioalgae Based Biorefineries: Opportunities and Challenges · doi:10.1007/978-981-97-1662-3_18 · Cited by 17 · see citing works
- Authors
- Chong, J. W. R., Khoo, K. S., Chew, K. W., Ting, H.-Y., & Show, P. L.
- Affiliations
- Wenzhou University (CN)
- Yuan Ze University (TW)
- Nanyang Technological University (SG)
- University of Technology Sarawak (MY)
- University of Nottingham (GB)
- Corresponding author
- Khoo, Kuan Shiong
- Show, Pau Loke
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 63
- Pages
- 108095
- ISSN
- 0734-9750 (ISSN-L)
- OpenAlex
- W4313531078
- Cited by
- 69
- References
- 152
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 18 · 2023: 18
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biotechnology Advances (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Digital Holography and Microscopy
- Keywords
- digital microscopy, image classification, harmful algal blooms, image preprocessing, feature extraction, noise reduction, convolutional neural networks, support vector machine, artificial neural networks, green biomass
- MeSH terms
- Algorithms, Humans, Image Processing, Computer-Assisted, Software, Neural Networks, Computer, Microalgae
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
