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  • Caspian Sea  (1)
  • Engineering
  • 1
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    Short communication: Application of machine-vision to assess weight of fish (Case study: Oncorhynchus mykiss) (2021)
    In:  http://aquaticcommons.org/id/eprint/22892 | 18721 | 2018-05-31 22:32:01 | 22892 | Iranian Fisheries Science Research Institute
    Details
    Publication Date: 2021-07-11
    Description: Computer Vision (CV) is a relatively young discipline which has been widely used to automate quality evaluation. CV inspection of fish and fish products can provide efficient, consistent and cost effective alternative, so efforts focused on speed and accuracy of machine vision as a substitute for human inspection of foods. Machine vision is explained as the construction of explicit informative and meaningful descriptions of a physical object via image analysis. Actually it encloses the capturing, processing and analysis of two-dimensional images, and by modeling human vision electronically perceives and understands images. ... This study tries to evaluate the relationship between weight of fish and visual features derived from image processing and present best fit relationship between weight and visual features.
    Keywords: Engineering ; Fisheries ; Image Processing ; Fish Weight ; MATLAB ; Iran
    Repository Name: AquaDocs
    Type: article , TRUE
    Format: application/pdf
    Format: application/pdf
    Format: 575-584
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  • 2
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    Assessing benthic health of hard substratum macrobenthic community using soft bottom indicators and their relationship with environmental condition (2021)
    In:  http://aquaticcommons.org/id/eprint/25996 | 18721 | 2018-10-19 17:50:43 | 25996 | Iranian Fisheries Science Research Institute
    Details
    Publication Date: 2021-07-24
    Description: This study aimed to assess ecological quality status of hard substratum macroinvertebrates communities of the Caspian Sea with three ecological indices and their relationship with environmental factors. For this purpose, benthic communities of the Caspian Sea basin were studied seasonally during 2014 in 8 sampling sites. Temperature, salinity, dissolved oxygen; pH, nitrate, nitrite, silicate and phosphate were measured as environmental factors. The benthic classification indices AMBI (AZTI Marine Biotic Index), M-AMBI (Multivariate AMBI) and BENTIX (BENthic IndeX) were applied to assess the ecological status of the studied area. Results showed low dissimilarity based on species composition and abundance among seasons, while all seasons discriminated clearly based on environmental factors. In addition, AMBI index was more successful to assess ecological health of hard substratum in the Caspian Sea basin than M-AMBI and BENTIX. Furthermore, AMBI showed high sensitivity to environmental variation. Results indicated that temperature, nitrate, silicate, phosphate and nitrite were the most important factors in the composition and abundance fluctuation of hard substratum macroinvertebrates communities, respectively.
    Keywords: Ecology ; Iran ; Caspian Sea ; Benthic health ; AMBI ; M-AMBI ; BENTIX ; Environmental factors ; Macrobenthic
    Repository Name: AquaDocs
    Type: article , TRUE
    Format: application/pdf
    Format: application/pdf
    Format: 641-656
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