CNN-Based Classification and Spatial Mapping of Aquaculture Pond Bottom Conditions

DOI

Bibliographic Information

Other Title
  • CNNを用いた養殖池水底環境の分類と空間分布の推定
Published
2025
DOI
  • 10.12792/iiae2025.025
Publisher
The Institute of Industrial Applications Engineers

Description

This study presents a practical approach for monitoring pond bottom conditions in kuruma shrimp (Marsupenaeus japonicus) aquaculture by combining an autonomous boat, an underwater camera, and deep learning-based image classification. Underwater videos were collected during autonomous navigation in a shrimp pond located in Amakusa, Japan. From the videos, 128×128 pixel image patches were manually extracted and labeled into four categories based on visual features: (1) gravel, (2) residual feed pellets, (3) white fungal-like matter, and (4) sludge-like sediment. A convolutional neural network (CNN) was developed and trained using these labeled patches. The trained model was then applied to classify pond bottom conditions by segmenting video frames into non-overlapping 128×128 patches and assigning class labels to each. The resulting spatial distribution maps revealed that residual feed tended to accumulate in low-flow regions, while sludge-like sediment was often found behind aerators. These spatial patterns indicated localized organic matter accumulation and environmental degradation, providing useful information for pond management. The proposed approach offers a low-cost and efficient tool for visualizing bottom conditions and supporting decision-making in aquaculture operations.

Journal

Details 詳細情報について

  • CRID
    1390024246560966784
  • DOI
    10.12792/iiae2025.025
  • ISSN
    2424211X
  • Text Lang
    ja
  • Data Source
    • JaLC
    • Crossref
  • Abstract License Flag
    Disallowed

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