Special Issue: Note

Mangrove species identification using Convolutional Neural Network

Adiguna Rahmat Nugraha, Nuryani Widagti, Riyo Wardoyo, I Wayan Eka Darmawan, I Nyoman Surana, Sarah Nuralifah, Devi Shifa Adillah, Alvira Nabilatul Aisyah, Ni Made Nia Bunga Surya Dewi, Edi Kurniawan, Frida Sidik

Published on: 20 March 2025

Page: 560 - 570

DOI: 10.6165/tai.2025.70.560

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    Abstract

    Mangroves are unique coastal ecosystems that are rich in biodiversity and have significant ecological value. Identifying mangrove species is important for many applications, such as biodiversity, restoration, and monitoring. As traditional methods are complicated and time-consuming, non-experts need an approach to identify mangroves in a timely and cost-effective manner. In this study, we created a deep learning approach for mangrove species identification based on leaf image recognition. We used digital images of mangrove leaves to identify mangrove species by applying Convolutional Neural Networks (CNN). A dataset of leaf images from 11 ‘true’ mangrove species found in Bali, Indonesia, was developed and divided into 80% for training and 20% for test datasets. About 20% of the training dataset was used for validation. Our results showed an accuracy of 98.86% on validation and 97.16% on a test set of images, promising possibilities for mangrove species identification. The finding indicates that the model effectively identifies mangrove species that are high in diversity and have morphological similarities.

    Keyword: Bali, Convolutional Neural Networks, deep learning, Indonesia, mangroves, plant identification

    Literature Cited

    Alias, N., Mansor, M., Hussin, M.A., Husin, T.M., Azman, N.Z.N., Hassan, N.A. 2020 Phenological study of mangrove species on the West Coast Area of Peninsular Malaysia. In Hamdan, O., Tariq Mubarak, H., Ismail, P. eds. Status of Mangroves in Malaysia p. 85 Tihani Cetak Sdn Bhd.

    Asnur, P., Kosasih, R., Madenda, S., Rahayu, D.A. 2023 Identification of mangrove tree species using deep learning method. Int. J. Adv. Appl. Sci. 12(2): 163–170.
    DOI: 10.11591/ijaas.v12.i2.pp163-170View Article Google Scholar

    Atsumi, K., Nishida, Y., Ushio, M., Nishi, H., Genroku, T., Fujiki, S. 2024 Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data. eLife 13: RP93694.
    DOI: 10.7554/eLife.93694View Article Google Scholar

    Azad, Md. S., Kamruzzaman, Md., Paul, S. K., Ahmed, S., Kanzaki, M. 2020 Vegetative and reproductive phenology of the mangrove Xylocarpus mekongensis Pierre in the Sundarbans, Bangladesh: Relationship with climatic variables. Reg. Stud. Mar. Sci. 38: 101359.
    DOI: 10.1016/j.rsma.2020.101359View Article Google Scholar

    Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A. C., Silliman, B.R. 2011 The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81(2): 169–193.
    DOI: 10.1890/10-1510.1View Article Google Scholar

    Bengar, J.Z., Weijer, J. van de, Fuentes, L.L., Raducanu, B. 2022 Class-Balanced Active Learning for Image Classification 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV): 3707–3716
    DOI: 10.1109/WACV51458.2022.00376View Article Google Scholar

    Camargo Maia, R., Coutinho, R. 2012 Structural characteristics of mangrove forests in Brazilian estuaries: A comparative study. Rev. Biol. Mar. Oceanogr. 47(1): 87–98.
    DOI: 10.4067/S0718-19572012000100008View Article Google Scholar

    Carneiro, T., Medeiros Da Nobrega, R. V., Nepomuceno, T., Bian, G.-B., De Albuquerque, V. H. C., Filho, P. P. R. 2018 Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access 6: 61677–61685.
    DOI: 10.1109/ACCESS.2018.2874767View Article Google Scholar

    Dissanayake, D. M. C., Kumara, W. G. C. W. 2021 Plant leaf identification based on machine learning algorithms. http://ir.lib.seu.ac.lk/handle/123456789/6611

    Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H. 2017 MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. https://doi.org/10.48550/ARXIV.1704.04861

    Kamruzzaman, Md., Paul, S. K., Ahmed, S., Azad, Md. S., Osawa, A. 2019 Phenology and litterfall production of Bruguiera sexangula Lour.) Poir. In the Sundarbans mangrove forests, Bangladesh. Forest Sci. Technol. 15(3): 165–172.
    DOI: 10.1080/21580103.2019.1638314View Article Google Scholar

    Kathiresan, K., Bingham, B.L. 2001 Biology of mangroves and mangrove Ecosystems. Adv. Mar. Biol. 40: 81–251.
    DOI: 10.1016/S0065-2881(01)40003-4View Article Google Scholar

    Lu, G., Zhang, W., Wang, Z. 2022 Optimizing depthwise separable convolution operations on GPUs. IEEE Trans. Parallel Distrib. Syst. 33(1): 70–87.
    DOI: 10.1109/TPDS.2021.3084813View Article Google Scholar

    Lucena, I., Maciel, V., Silva, J., Galvincio, J., Pimentel, R. 2011 Leaf structure of mangrove species to understand the spectral responses. J. Hyperspectr. Remote Sens. 1(2): 19–31.
    DOI: 10.5935/2237-2202.20110002View Article Google Scholar

    Lugo, A.E., Snedaker, S.C. 1974 The ecology of mangroves. Annu. Rev. Ecol. Evol. Syst. 5(1): 39–64.
    DOI: 10.1146/annurev.es.05.110174.000351View Article Google Scholar

    Michele, A., Colin, V., Santika, D.D. 2019 MobileNet convolutional neural networks and support vector machines for palmprint recognition. Procedia Comput. Sci. 157: 110–117.
    DOI: 10.1016/j.procs.2019.08.147View Article Google Scholar

    Nascimento, M.G.P., Mayo, S.J., de Andrade, I.M. 2021 Distinguishing the Brazilian mangrove species Avicennia germinans and Avicennia schaueriana (Acanthaceae) by elliptic Fourier analysis of leaf shape. Feddes Repert. 132(2): 77–107.
    DOI: 10.1002/fedr.202000025View Article Google Scholar

    Polidoro, B.A., Carpenter, K.E., Collins, L., Duke, N.C., Ellison, A.M., Ellison, J.C., Farnsworth, E.J., Fernando, E.S., Kathiresan, K., Koedam, N.E., Livingstone, S.R., Miyagi, T., Moore, G.E., Nam, V.N., Ong, J.E., Primavera, J.H., Iii, S.G.S., Sanciangco, J.C., Sukardjo, S., Yong, J.W.H., Hansen, D.M. 2010 The loss of species: Mangrove extinction risk and geographic areas of global concern. PLOS ONE 5(4): e10095.
    DOI: 10.1371/journal.pone.0010095View Article Google Scholar

    Praveen Gujjar, J., Prasanna Kumar, H.R., Chiplunkar, N.N. 2021 Image classification and prediction using transfer learning in colab notebook. Global Transitions Proceedings 2(2): 382–385.
    DOI: 10.1016/j.gltp.2021.08.068View Article Google Scholar

    Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C. 2018 MobileNetV2: Inverted residuals and linear bottlenecks 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition: 4510–4520.
    DOI: 10.1109/CVPR.2018.00474View Article Google Scholar

    Sidik, F., Kurniawan, E., Dewi, N. M. N. B. S., Widagti, N., Nugraha, A. R., Surana, N. 2023 Dataset mangrove Budeng Bali 2023 Dataset hdl:20.500.12690/RIN/IZ1SNL RIN Dataverse. https://data.brin.go.id/

    Tomlinson, P. B. 1994 The Botany of Mangroves 1st pbk. ed Cambridge University Press.

    Tu, C.-H., Lee, J.-H., Chan, Y.-M., Chen, C.-S. 2020 Pruning Depthwise Separable Convolutions for MobileNet Compression. 2020 International Joint Conference on Neural Networks IJCNN: 1–8.
    DOI: 10.1109/IJCNN48605.2020.9207259View Article Google Scholar

    Urbano, F., Viterbi, R., Pedrotti, L., Vettorazzo, E., Movalli, C., Corlatti, L. 2024 Enhancing biodiversity conservation and monitoring in protected areas through efficient data management. Environ. Monit. Assess. 196(1): 12.
    DOI: 10.1007/s10661-023-11851-0View Article Google Scholar

    Viodor, A. C. C., Aliac, C. J. G., Santos-Feliscuzo, L. T. 2022 Mangrove Species Identification Using Deep Neural Network. 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering ICITISEE: 1–6.
    DOI: 10.1109/ICITISEE57756.2022.10057793View Article Google Scholar

    Viodor, A. C. C., Aliac, C. J. G., Santos-Feliscuzo, L. T. 2023 Identifying Mangrove Species using Deep Learning Model and Recording for Diversity Analysis: A Mobile Approach. 2023 IEEE Open Conference of Electrical, Electronic and Information Sciences eStream): 1–6.
    DOI: 10.1109/eStream59056.2023.10134992View Article Google Scholar

    Wan, L., Zhang, H., Lin, G., Lin, H. 2019 A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image. Ann. GIS, 25(1): 45–55.
    DOI: 10.1080/19475683.2018.1564791View Article Google Scholar

    Wani, M.A., Bhat, F.A., Afzal, S., Khan, A.I. 2020 Advances in Deep Learning. Springer.

    Xie, X., Zhao, G., Wei, W., Huang, W. 2022 MobileNetV2 Accelerator for Power and Speed Balanced Embedded Applications. 2022 IEEE 2nd International Conference on Data Science and Computer Application ICDSCA: 134–139.
    DOI: 10.1109/ICDSCA56264.2022.9988258View Article Google Scholar