Weight Measurement of Holothuria Scabra Jaeger, 1833 Utilizing the Surface Area of Digitized Image Captured under Water

Mary Jane Magno-Tan, A. Coronado, R. R. García, May D. Maulani
{"title":"Weight Measurement of Holothuria Scabra Jaeger, 1833 Utilizing the Surface Area of Digitized Image Captured under Water","authors":"Mary Jane Magno-Tan, A. Coronado, R. R. García, May D. Maulani","doi":"10.1145/3033288.3033290","DOIUrl":null,"url":null,"abstract":"In culturing sea cucumber (Holothuria scabra), weight is an essential parameter for selection of breeders as well as in determining the correct time of harvest. H. scabra eviscerate under stressful conditions and micturate when taken out of water, which leads to erratic weight measurement and obscure data to select individuals for harvest. This research aims to automatically compute the weight of H. scabra with a new algorithm that utilizes only the surface area, through the image captured by a regular camera while the specimen is submerged under water. Digital images of one hundred seventy-seven (177) healthy adult H. scabra were converted to binary images and used to measure the individual length, width and surface area through pixel analysis. The weight of H. scabra was computed using the equation: Weight[g]=C1+(C2*Surface Area), which is generated through linear regression wherein C1 and C2 has constant values of -51.840 and 3.717, respectively. Results showed that the surface area and weight of H. scabra under normal culture condition is highly correlated (R2=0.90). Moreover, error analysis revealed that the accuracy of the software in determining the length, width, surface area and weight of H. scabra was 94.46%, 94.16%, 94.07%, and 83.79% respectively. Analysis of Variance (ANOVA) showed that comparable data were obtained between actual measurements and software generated data for length (α0.05<0.270), width (α0.05<0.388), surface area (α0.05<0.924) and weight (α0.05<0.509) of H. scabra.","PeriodicalId":253625,"journal":{"name":"International Conference on Network, Communication and Computing","volume":"317 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Network, Communication and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3033288.3033290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In culturing sea cucumber (Holothuria scabra), weight is an essential parameter for selection of breeders as well as in determining the correct time of harvest. H. scabra eviscerate under stressful conditions and micturate when taken out of water, which leads to erratic weight measurement and obscure data to select individuals for harvest. This research aims to automatically compute the weight of H. scabra with a new algorithm that utilizes only the surface area, through the image captured by a regular camera while the specimen is submerged under water. Digital images of one hundred seventy-seven (177) healthy adult H. scabra were converted to binary images and used to measure the individual length, width and surface area through pixel analysis. The weight of H. scabra was computed using the equation: Weight[g]=C1+(C2*Surface Area), which is generated through linear regression wherein C1 and C2 has constant values of -51.840 and 3.717, respectively. Results showed that the surface area and weight of H. scabra under normal culture condition is highly correlated (R2=0.90). Moreover, error analysis revealed that the accuracy of the software in determining the length, width, surface area and weight of H. scabra was 94.46%, 94.16%, 94.07%, and 83.79% respectively. Analysis of Variance (ANOVA) showed that comparable data were obtained between actual measurements and software generated data for length (α0.05<0.270), width (α0.05<0.388), surface area (α0.05<0.924) and weight (α0.05<0.509) of H. scabra.
利用水下捕获的数字化图像的表面积测量1833年全息图的重量
在养殖海参(Holothuria scabra)中,重量是选择育种者和确定正确收获时间的重要参数。在有压力的条件下,黄貂鱼会内脏脱落,在离开水的情况下会发生micate,这导致体重测量不稳定,并且在选择收获个体时数据模糊。本研究旨在通过常规相机在水下拍摄的图像,利用仅利用表面面积的新算法自动计算剑鞘鱼的重量。将177例健康成年糙皮糙皮虫的数字图像转换为二值图像,通过像素分析测量个体的长度、宽度和表面积。粗丝茅的权重计算公式为:weight [g]=C1+(C2*Surface Area),通过线性回归得到,其中C1和C2的定值分别为-51.840和3.717。结果表明:在正常培养条件下,黄颡鱼的表面积与体重呈高度相关(R2=0.90);误差分析结果表明,该软件测定黄刺草长度、宽度、表面积和重量的准确度分别为94.46%、94.16%、94.07%和83.79%。方差分析(ANOVA)结果表明,实际测量数据与软件生成数据的长度(α0.05<0.270)、宽度(α0.05<0.388)、表面积(α0.05<0.924)和重量(α0.05<0.509)具有可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信