A Network based Approach for Automated Identification of Calanoid Copepods using Deep Learning
Keywords:
Artificial intelligence, Calanoid copepods, Convolutional neural network, Deep learningAbstract
In recent years, deep learning techniques have played an important role in the biological field. The present study proposes a convolutional neural network approach for identification of calanoid copepods Temora discaudata and Canthocalanus pauper. T. discaudata and C. pauper classifies with the image dataset to develop for classifying the species. Nowadays, ecological science is improved by advances in Artificial Intelligence (AI) for the classification of different species images. The digital image technique includes augmentation, pre-processing, segmentation, and classification, and is implemented based on deep learning algorithms to improve classification of species with different features with Convolutional Neural Networks (CNNs). This study proposed pre processing with the size of 64×64 and 224×224 of the calanoid species, then augmentation followed by classification. Also, image processing is focused to implement the original image to the binary mask for yielding better accuracy. During the classification of the T. discaudata and C. pauper images, the macro average and weighted average are calculated for finding 90% and 93% accuracy, respectively of the training model. The conventional method of identification of calanoid copepods is tedious, while, the CNN can automatically predict the species features from the data set. Finally, the experiment analyzed the T. discaudata and C. pauper datasets in the technical aspect of digital image processing techniques in Artificial Intelligence.
References
Walter TC, Boxshall G. World of Copepods Database. Accessed at https://www.marinespecies.org/copepoda. 2022. https://doi.org/10.14284/356.
Shanthi M, Ramanibai R. Studies on copepods from Chennai coast (Coovum and Adyar), Bay of Bengal during the cruise. Curr Res J Biol Sci 2011; 3(1):132-136.
Kwok KWH, Souissi S, Dur G, Won EJ, Lee JS. Copepods as references species in estuarine and marine waters, In: Amiard-Triquet C, Amiard JC, Mouneyrac C. (eds.) Aquatic ecotoxicology: advancing tools for dealing with emerging risks, Academic Press, London. 2015;281-308.
Venkataraman K, Raghunathan C. Coastal and Marine Biodiversity of India. In: Venkataraman, K., Sivaperuman, C, eds. Marine faunal diversity in India, Academic Press. 2015; 303-348.
Jeyaraj N, Ravikumar S, Rajthilak C, Kumar SP, Santhanam P. Abundance and diversity of zooplankton along the Gulf of Mannar region, Southeast coast of India. Int J Mar Sci 2016; 6(28):1-9
Bradford-Grieve JM, Boxshall GA, Ahyong ST, Ohtsuka S. Cladistic analysis of the calanoid Copepoda. Invert System. 2010; 24:291–321.
Andronov VN. Novye vidy Diaixidae i Stephidae (Copepoda) iz severnykh raionov Indiiskogo okeana. New species of Diaixidae and Stephidae (Copepoda) from the northern regions of the Indian Ocean. Zoologi Zhur. 1974;53(3):460-464.
Fosshagen A, Iliffe TM. Two new genera of Calanoida and a new order of Copepoda, Platycopioida, from marine caves on Bermuda. Sarsia 1985;70(4): 345-358.
Park T. Phylogeny of calanoid copepods. Syllogeus. 1986;58: 191– 196.
Frost BW, Fleminger A. A revision of the genus Clausocalanus (Copepoda: Calanoida) with remarks on distributional patterns in diagnostic characters. Bull Scripps Inst Oceano 1968;12:1–235.
Wiebe PH, Bucklin A, Benfield M. Sampling, preservation and counting of samples II. In: Castellani C, Edwards M, eds. Zooplankton in marine plankton, Oxford University Press UK. 2017;104–135.
Culverhouse PF, Williams R, Reguera B, Herry V, González-Gil S. Do experts make mistakes? A comparison of human and machine identification of dinoflagellates. Mar Ecol Prog Ser 2003;247:17-25
Hurwitz J, Kirsch D. Machine learning for dummies. Hoboken New Jersey USA, John Wiley & Sons Inc. 2018;75.
Tang X, Lin F, Samson S, Remsen A. Binary plankton image classification. IEEE J Ocean Engg 2006;31(3):728-35.
Zhao F, Lin F, Seah H. Bagging based plankton image classification. Proc IEEE Int Conf Image Processing 2009;2517-20.
Goodfellow I, Bengio Y, Courville A. Deep learning, MIT press. 2016;330-372.
Al-jabery KK, Obafemi-Ajayi T, Olbricht GR, Wunsch II DC. Computational learning approaches to data analytics in biomedical applications, Academic Press. 2020;101-123.
Cheng K, Cheng X, Wang Y, Bi H, Benfield MC. Enhanced convolutional neural network for plankton identification and enumeration. Plos One 2019;14(7):e0219570.
Pedraza A, Bueno G, Deniz O, Cristóbal G, Blanco S, Borrego-Ramos M. Automated diatom classification (Part B): A Deep Learning Approach, Appl Sci 2017;7(5):460.
Wu, CH Artificial neural networks for molecular sequence analysis. J Comput Chem 1997;21(4): 237-256.
Haykin S. Neural Networks, a comprehensive foundation. Int J Neural Sys 1994;5(4): 363-364
Freeman JA, Skapura DM. Neural networks: algorithms, applications, and programming techniques. Addison-Wesley Publishing Company USA. 1991
Wang J, Lin C, Ji L, Liang AA. New automatic identification system of insect images at the order level. Knowledge-Based Systems. 2012;33: 102-110.
Kiranyaz, S., Birinci, M., Gabbouj, M. Perceptual color descriptor based on spatial distribution: A top-down approach. Image Vis Comp 2010;28(8): 1309-1326.
Coltelli P, Barsanti L, Evangelista V, Frassanito AM, Gualtieri P. Water monitoring: automated and real time identification and classification of algae using digital microscopy. Environ Sci Processes Impacts 2014;(11):2656-2665.
Mosleh MAA, Manssor H, Sorayya M, Milow P, Aishah S. A preliminary study on automated freshwater algae recognition and classification system. BMC Bioinform 2012;13(Suppl 17):S25.
Hernández-Serna A, Jiménez-Segura LF. Automatic identification of species with neural networks. PeerJ 2004;2:e563.
Alsmadi MK, Omar KB, Noah SA, Almarashdeh I. Fish recognition based on robust features extraction from size and shape measurements using neural network. J Comp Sci 2010;6(10): 1088-1094
Ginoris YP, Amaral AL, Nicolau A, Coelho MAZ, Ferreira EC. Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees. Anal Chim Acta 2007;595:160-169
Culverhouse PF, Simpson RG, Ellis R, Lindley JA, Williams R, Parisini T, Reguera B, Bravo I, Zoppoli R, Earnshaw G, McCall H, Smith G. Automatic classification of field-collected dinoflagellates by artificial neural network. Mar Ecol Prog Ser 1996;139:281-287.
Yang YS, Park DK, Kim HC, Choi MH, Chai JY. Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network. IEEE Trans Biomed Eng 2001;48(6): 718-730.
Leow LK, Chew LL, Chong VC, Dhillon SK. Automated identification of copepods using digital image processing and artificial neural network. BMC Bioinfo 2015; 16 (Suppl 18):S4.
Hu Q, Davis C. Automatic plankton image recognition with co-occurrence matrices and support vector machine. Mar Ecol Prog Ser 2005;295: 21-31
Cowen RK, Sponaugle S, Robinson KL, Luo J. PlanktonSet 1.0: Plankton imagery data collected from F.G. Walton Smith in Straits of Florida from 2014-06-03 to 2014-06-06 and used in the 2015 National Data Science Bowl (NODC Accession 0127422). NOAA National Centers for Environmental Information. Dataset. 2015. https://doi.org/10.7289/V5D21VJD.
Faillettaz R, Picheral M, Luo JY, Guigand C, Cowen RK, Irisson J-O. Imperfect automatic image classification successfully describes plankton distribution patterns. Meth Oceanogr. 2016;15-16:60-77.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521: 436-444.
Fernandes JA, Irigoien X, Boyra G, Lozano JA, Inza I. Optimizing the number of classes in automated zooplankton classification. Journal of Plankton Research 2009;31(1): 19-29
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