Evaluation of body weight of sea cucumber <i>Apostichopus japonicus</i> by computer vision -- Journal of Oceanology and Limnology,2015,33(1):114-120

	Cite this paper:
	LIU Hui, XU Qiang, LIU Shilin, ZHANG Libin, YANG Hongsheng. Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision[J]. Journal of Oceanology and Limnology, 2015, 33(1): 114-120

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

LIU Hui^1,2, XU Qiang¹, LIU Shilin¹, ZHANG Libin¹, YANG Hongsheng¹

1 Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China

Abstract:

Apostichopus 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 (R² =0.99) and compatible with the power model (R² =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.

Key words: Apostichopus japonicas|wet weight|computer vision|support vector machine

Received: 2014-04-07 Revised: 2014-06-24

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Articles by YANG Hongsheng

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