Water Color Identification System for Monitoring Aquaculture Farms

Hsiang Chieh Chen, Sheng Yao Xu, Kai Han Deng

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This study presents a vision-based water color identification system designed for monitoring aquaculture ponds. The algorithm proposed in this system can identify water color, which is an important factor in aquaculture farming management. To address the effect of outdoor lighting conditions on the proposed system, a color correction method using a color checkerboard was introduced. Several candidates for water-only image patches were extracted by performing image segmentation and fuzzy inferencing. Finally, a deep learning-based model was employed to identify the color of these patches and then find the representative color of the water. Experiments at different aquaculture sites verified the effectiveness of the proposed system and its algorithm. The color identification accuracy exceeded 96% for the test data.

Original languageEnglish
Article number7131
JournalSensors (Switzerland)
Issue number19
StatePublished - Oct 2022


  • aquaculture
  • color correction
  • color identification
  • image segmentation
  • smart agriculture
  • water color


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