Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision

Hui Liu; Qiang Xu; Shilin Liu; Libin Zhang; Hongsheng Yang

doi:10.1007/s00343-015-4080-3

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Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision

Updated：2023-05-16

- Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision
- Journal of Oceanology and Limnology Vol. 33, Issue 1, Pages: 114-120(2015)
- Affiliations：
  
  1. Key Laboratory of Marine Ecology and Environmental Sciences Institute of Oceanology Chinese Academy of Sciences
  2. University of Chinese Academy of Sciences
- Author bio：
- Funds：
- DOI：10.1007/s00343-015-4080-3
  CLC：
- Online First：27 October 2014，
  
  Published：2015
- Accepted：
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Liu, H., Xu, Q., Liu, S. et al. Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision., Chin. J. Ocean. Limnol. 33, 114–120 (2015). https://doi.org/10.1007/s00343-015-4080-3
DOI：

Liu, H., Xu, Q., Liu, S. et al. Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision., Chin. J. Ocean. Limnol. 33, 114–120 (2015). https://doi.org/10.1007/s00343-015-4080-3 DOI：

摘要

Abstract

A postichopus japonicus (Holothuroidea

Echinodermata) is an ecological and economic species in East Asia. Conventional biometric monitoring method includes diving for samples and weighing above water

with highly variable in weight measurement due to variation in the quantity of water in the respiratory tree and intestinal content of this species. Recently

video survey method has been applied widely in biometric detection on underwater benthos. However

because of the high flexibility of A. japonicus body

video survey method of monitoring is less used in sea cucumber. In this study

we designed a model to evaluate the wet weight of A. japonicus

using machine vision technology combined with a support vector machine (SVM) that can be used in field surveys on the A. japonicus population. Continuous dorsal images of free-moving A. japonicus individuals in seawater were captured

which also allows for the development of images of the core body edge as well as thorn segmentation. Parameters that include body length

body breadth

perimeter and area

were extracted from the core body edge images and used in SVM regression

to predict the weight of A. japonicus and for comparison with a power model. Results indicate that the use of SVM for predicting the weight of 33 A. japonicus individuals is accurate (R 2=0.99) and compatible with the power model (R 2 =0.96). The image-based analysis and size-weight regression models in this study may be useful in body weight evaluation of A. japonicus in lab and field study.

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