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

• Published in: Chinese journal of oceanology and limnology 2015-01, Vol.33 (1), p.114-120
• Main Author: 刘辉 许强 刘石林 张立斌 杨红生
• Format: Article
• Language: English
• Subjects: Apostichopus japonicus; benthic organisms; Benthos; Biology; Biometrics; Biometry; Body length; Body weight; Chemical analysis; Commercial species; Computer vision; Earth and Environmental Science; Earth Sciences; Echinodermata; Holothurioidea; Holothuroidea; Image processing; Machine vision; Marine; Marine biology; Marine invertebrates; Monitoring; Monitoring methods; Oceanography; prediction; Regression analysis; Regression models; Regressions; respiratory system; Seawater; Support vector machines; Surveys; Water analysis; water quantity; Weight measurement; Wet weight; 体重; 刺参; 支持向量机; 海参纲; 生态经济树种; 计算机视觉; 评估; 重量测量
• ISSN: 0254-4059; 2096-5508; 1993-5005; 2523-3521
• DOI: 10.1007/s00343-015-4080-3

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 ofA. 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 ofA. japonicus in lab and field study.