Automatic Classification of Desmids using Transfer Learning


  • Rajmohan Pardeshi Dept. of CS and IT Dr. Babasaheb Ambedkar Marathwada University, Aurangabad
  • Prapti Deshmukh MGM University, Aurangabad , India



Deep Learning, Transfer Learning, Algae Classification, Desmids Classification, CNN Comparative study


This research paper presents a novel approach to classifying microscopic images of desmids using transfer learning and convolutional neural networks (CNNs). The purpose of this study was to automate the tedious task of manually classifying microscopic algae and improve our understanding of water quality in aquatic ecosystems. To accomplish this, we utilized transfer learning to fine-tune 13 pre-trained CNN models on a dataset of five categories of desmids. We evaluated the performance of our models using several metrics, including accuracy, precision, recall, and F1-score. Our results show that transfer learning can significantly improve the classification accuracy of microscopic images of desmids, and efficient CNN models can further enhance performance. The practical implications of this research include a more efficient and accurate method for classifying microscopic algae and assessing water quality. The theoretical implications include a better understanding of the application of transfer learning and CNNs in image classification. This research contributes to both theory and practice by providing a new method for automating the classification of microscopic algae and improving our understanding of aquatic ecosystems


Download data is not yet available.

Author Biographies

Rajmohan Pardeshi, Dept. of CS and IT Dr. Babasaheb Ambedkar Marathwada University, Aurangabad



Prapti Deshmukh, MGM University, Aurangabad , India

Dean, Applied Sciences,

Professor in Computer Science and IT

MGM University, Aurangabad, Maharashtra, India



Adejimi, O. E., Sadhasivam, G., Schmilovitch, Z., Shapiro, O. H., & Herrmann, I. (2023). Applying hyperspectral transmittance for inter-genera classification of cyanobacterial and algal cultures. Algal Research, 71, 103067.

Agarwal, V., Chávez-Casillas, J., & Mouw, C. B. (2023). Sub-monthly prediction of harmful algal blooms based on automated cell imaging. Harmful Algae, 122, 102386.

Ali, Abdullah, S., Khan, Z., Hussain, A., Athar, A., & Kim, H. C. (2022). Computer Vision Based Deep Learning Approach for the Detection and Classification of Algae Species Using Microscopic Images. Water, 14(14), 2219.

Barsanti, L., Birindelli, L., & Gualtieri, P. (2021). Water monitoring by means of digital microscopy identification and classification of microalgae. Environmental Science: Processes & Impacts, 23(10), 1443–1457.

Cai, H., Shan, S., & Wang, X. (2022, July). Rapid detection for optical micrograph of plankton in ballast water based on neural network. Algal Research, 66, 102811.

Chong, J. W. R., Khoo, K. S., Chew, K. W., Ting, H. Y., & Show, P. L. (2023, March). Trends in digital image processing of isolated microalgae by incorporating classification algorithm. Biotechnology Advances, 63, 108095.

Chong, J. W. R., Khoo, K. S., Chew, K. W., Vo, D. V. N., Balakrishnan, D., Banat, F., Munawaroh, H. S. H., Iwamoto, K., & Show, P. L. (2023). Microalgae identification: Future of image processing and digital algorithm. Bioresource Technology, 369, 128418.

Coesel, P. F. M. (1996). 5. Biogeography of desmids. Hydrobiologia, 336(1–3), 41–53.

Domozych, D. S., & Domozych, C. R. (2007). Desmids and Biofilms of Freshwater Wetlands: Development and Microarchitecture. Microbial Ecology, 55(1), 81–93.

Gaur, A., Pant, G., & Jalal, A. S. (2021). Morphology-based Identification and Classification of Harmful Bloom Forming Algae through Inception V3 Convolution Neural Network. 2021 5th International Conference on Information Systems and Computer Networks (ISCON).

Gaur, A., Pant, G., & Jalal, A. S. (2022). Computer-aided cyanobacterial harmful algae blooms (CyanoHABs) studies based on fused artificial intelligence (AI) models. Algal Research, 67, 102842.

Gaur, A., Pant, G., & Jalal, A. S. (2023). Comparative assessment of artificial intelligence (AI)-based algorithms for detection of harmful bloom-forming algae: an eco-environmental approach toward sustainability. Applied Water Science, 13(5).

George Karimpanal, T., & Bouffanais, R. (2018). Self-organizing maps for storage and transfer of knowledge in reinforcement learning. Adaptive Behavior, 27(2), 111–126.

Gerdan Koc, D., Koc, C., & Ekinci, K. (2023). Fusion-based machine learning approach for classification of algae varieties exposed to different light sources in the growth stage. Algal Research, 71, 103087.

Goldberg, S.J.; Kirby, J.T.; Licht, S.C (2016). Applications of Aerial Multi-Spectral Imagery for Algal Bloom Monitoring in Rhode Island SURFO Technical Report No. 16-01. (n.d.). DigitalCommons@URI.

Gong, X., Ma, C., Sun, B., & Zhang, J. (2023). An Efficient Self-Organized Detection System for Algae. Sensors, 23(3), 1609.

Guo, J., Ma, Y., & Lee, J. H. (2021). Real-time automated identification of algal bloom species for fisheries management in subtropical coastal waters. Journal of Hydro-Environment Research, 36, 1–32.

Guterres, B., khalid, S., Pias, M., & Botelho, S. (2023). A data integration pipeline towards reliable monitoring of phytoplankton and early detection of harmful algal blooms. Climate Change AI.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and. . . OpenReview.

Khan, Abdullah, Z., Mumtaz, W., Mumtaz, A. S., Bhattacharjee, S., & Kim, H. C. (2022). Multiclass-Classification of Algae using Dc-GAN and Transfer Learning. 2022 2nd International Conference on Image Processing and Robotics (ICIPRob).

Kim, S., Nam, J., & Ko, B. C. (2022). ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder. PMLR.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.

Kudela, R. M., Palacios, S. L., Austerberry, D. C., Accorsi, E. K., Guild, L. S., & Torres-Perez, J. (2015). Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters. Remote Sensing of Environment, 167, 196–205.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Lekki, J., Ruberg, S., Binding, C., Anderson, R., & Vander Woude, A. (2019). Airborne hyperspectral and satellite imaging of harmful algal blooms in the Great Lakes Region: Successes in sensing algal blooms. Journal of Great Lakes Research, 45(3), 405–412.

Li, S. F., Jia, Q., & Liang, H. (2014). Research of Red Tide Algae Images Feature Selection Method Based on ReliefF and SBS. Applied Mechanics and Materials, 507, 806–809.

Liao, Y., Yu, N., Zhou, G., Wu, Y., & Wang, C. (2022). A wireless multi-channel low-cost lab-on-chip algae culture monitor AIoT system for algae farm. Computers and Electronics in Agriculture, 193, 106647.

Liu, Z., Tan, Y., He, Q., & Xiao, Y. (2022). SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection. IEEE Transactions on Circuits and Systems for Video Technology, 32(7), 4486–4497.

Luo, J., Ni, G., Zhang, Y., Wang, K., Shen, M., Cao, Z., Qi, T., Xiao, Q., Qiu, Y., Cai, Y., & Duan, H. (2023,A new technique for quantifying algal bloom, floating/emergent and submerged vegetation in eutrophic shallow lakes using Landsat imagery. Remote Sensing of Environment, 287, 113480.

Pant, G., Yadav, D., & Gaur, A. (2020, June). ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum. Algal Research, 48, 101932.

Pardeshi, R., & Deshmukh, P. D. (2020). Classification of Microscopic Algae: An Observational Study with AlexNet. Advances in Intelligent Systems and Computing, 309–316.

Park, J., Lee, H., Park, C. Y., Hasan, S., Heo, T. Y., & Lee, W. H. (2019, June 28). Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network. Water, 11(7), 1338.

Piazza, G., Valsecchi, C., & Sottocornola, G. (2021, December 3). Deep Learning Applied to SEM Images for Supporting Marine Coralline Algae Classification. Diversity, 13(12), 640.

Prescott, G. W. (1948, December). Desmids. The Botanical Review, 14(10), 644–676.

Qian, P., Zhao, Z., Liu, H., Wang, Y., Peng, Y., Hu, S., Zhang, J., Deng, Y., & Zeng, Z. (2020, July). Multi-Target Deep Learning for Algal Detection and Classification. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., & Dollar, P. (2020, June). Designing Network Design Spaces. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Reimann, R., Zeng, B., Jakopec, M., Burdukiewicz, M., Petrick, I., Schierack, P., & Rödiger, S. (2020, June). Classification of dead and living microalgae Chlorella vulgaris by bioimage informatics and machine learning. Algal Research, 48, 101908.

Simonyan, K., & Zisserman, A. (2014, September 4). Very Deep Convolutional Networks for Large-Scale Image Recognition.

Sonmez, M. E., Eczac?oglu, N., Gumu?, N. E., Aslan, M. F., Sabanci, K., & A?ikkutlu, B. (2022). Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups. Algal Research, 61, 102568.

Szegedy, C., Wei Liu, Yangqing Jia, Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015, June). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Tan, M., & Le, Q. V. (2019, May 28). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019, June). MnasNet: Platform-Aware Neural Architecture Search for Mobile. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Tangsuksant, & Sarakon. (2023). Microalgae Detection by Digital Image Processing and Artificial Intelligence, International Conference on Artificial Life and Robotics (ICAROB2023), Feb. 9 to 12, on line, Oita, Japan, Retrieved May 9, 2023, from

Wang, Z., Yang, S., Shi, M., & Qin, K. (2022). An Image Augmentation Method Based on Limited Samples for Object Tracking Based on Mobile Platform. Sensors, 22(5), 1967.

West, J., Ventura, D. and Warnick, S. (2007) Spring Research Presentation A Theoretical Foundation for Inductive Transfer. Brigham Young University, College of Physical and Mathematical Sciences, 32. - References - Scientific Research Publishing. (n.d.). Retrieved May 9, 2023, from

Won;Kim Kwangtae;Nam Jiwon;You Jungsu;Baek Park, J. S. (2021). Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3 -Journal of Korean Society on Water Environment | Korea Science. Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3 -Journal of Korean Society on Water Environment | Korea Science.

Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Xu, L., Xu, L., Chen, Y., Zhang, Y., & Yang, J. (2022). Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database. ACS ES&T Water, 2(11), 1921–1928.

Yang, M., Wang, W., Gao, Q., Zhang, L., Ji, Y., & Geng, S. (2021). Automatic Recognition of Harmful Algae Images Using Multiple CNNs. 2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI).

Yang, M., Wang, W., Gao, Q., Zhao, C., Li, C., Yang, X., Li, J., Li, X., Cui, J., Zhang, L., Ji, Y., & Geng, S. (2022). Automatic identification of harmful algae based on multiple convolutional neural networks and transfer learning. Environmental Science and Pollution Research, 30(6), 15311–15324.

Yuan, A., Wang, B., Li, J., & Lee, J. H. (2023). A low-cost edge AI-chip-based system for real-time algae species classification and HAB prediction. Water Research, 233, 119727.

Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.




How to Cite

Pardeshi, R., & Deshmukh, P. (2023). Automatic Classification of Desmids using Transfer Learning . Journal of Applied Engineering and Technological Science (JAETS), 4(2), 885–894.