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LWDS: lightweight DeepSeagrass technique for classifying seagrass from underwater images

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Abstract

In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous fish, endangered sea cows, Dugong dugon, and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on lightweight DeepSeagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classifies the seagrasses quickly and with lesser parameters. The DeepSeagrass dataset is used to test LWDS's applicability.

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The dataset is publicly available.

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Authors and Affiliations

  1. Francis Xavier Engineering College, Vannarpet, Tirunelveli, Tamilnadu, 627003, India

    M. Asha Paul

  2. Department of Computer Science and Engineering, AMET University, Chennai, Tamilnadu, India

    K. Sampath Kumar

  3. School of Computing/Department of Computer Science and Engineering, Galgotias University, Delhi, India

    Shrddha Sagar

  4. Sathyabama Institute of Science and Technology (Deemed to be University), Chennai, Tamil Nadu, 600119, India

    S Sreeji

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  1. M. Asha Paul
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  2. K. Sampath Kumar
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  3. Shrddha Sagar
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  4. S Sreeji
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Contributions

Asha Paul is responsible for designing the framework, validating the results, and writing the article. Sampath Kumar, Shrdda Sagar, and Sreeji are responsible for collecting the information required for the work and critical review.

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Correspondence to M. Asha Paul.

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Paul, M.A., Kumar, K.S., Sagar, S. et al. LWDS: lightweight DeepSeagrass technique for classifying seagrass from underwater images. Environ Monit Assess 195, 614 (2023). https://doi.org/10.1007/s10661-023-11183-z

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