Jojo C. Garanganao, Lea P. Ymalay, A. P. Delima, Jan Carlo T. Arroyo
{"title":"基于二维计算机视觉的罗非鱼体重估计系统","authors":"Jojo C. Garanganao, Lea P. Ymalay, A. P. Delima, Jan Carlo T. Arroyo","doi":"10.46338/ijetae0223_17","DOIUrl":null,"url":null,"abstract":"This study aimed to develop a Tilapia Weight Estimation System using 2D Computer Vision, for the AgriAqua Research and Technology Center located at Nanga, Pototan, Iloilo, Philippines.A system estimated the weight of live tilapia, and images were captured in a free-swimming aquarium. The study established the relationship between the fish's shape and its mass in order to estimate the weight of the fish. The dataset used in the system is composed of several tilapia images acquired from specimens with weights ranging from approximately 120-250 grams. As part of the evaluation, this study determined the accuracy of three regression models: Linear, Multiple, and Polynomial regressions in estimating the weight of a freely-swimming Tilapia. In addition to the R2 and P-value, the models were also compared in terms of RMSE, MAE, MARE, MXAE, and MXRE. The system’s quality was also evaluated according to the standards for computer software set by ISO 25010 International Quality Standards","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tilapia Weight Estimation System Using 2D Computer Vision\",\"authors\":\"Jojo C. Garanganao, Lea P. Ymalay, A. P. Delima, Jan Carlo T. Arroyo\",\"doi\":\"10.46338/ijetae0223_17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aimed to develop a Tilapia Weight Estimation System using 2D Computer Vision, for the AgriAqua Research and Technology Center located at Nanga, Pototan, Iloilo, Philippines.A system estimated the weight of live tilapia, and images were captured in a free-swimming aquarium. The study established the relationship between the fish's shape and its mass in order to estimate the weight of the fish. The dataset used in the system is composed of several tilapia images acquired from specimens with weights ranging from approximately 120-250 grams. As part of the evaluation, this study determined the accuracy of three regression models: Linear, Multiple, and Polynomial regressions in estimating the weight of a freely-swimming Tilapia. In addition to the R2 and P-value, the models were also compared in terms of RMSE, MAE, MARE, MXAE, and MXRE. The system’s quality was also evaluated according to the standards for computer software set by ISO 25010 International Quality Standards\",\"PeriodicalId\":169403,\"journal\":{\"name\":\"International Journal of Emerging Technology and Advanced Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Emerging Technology and Advanced Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46338/ijetae0223_17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Emerging Technology and Advanced Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46338/ijetae0223_17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tilapia Weight Estimation System Using 2D Computer Vision
This study aimed to develop a Tilapia Weight Estimation System using 2D Computer Vision, for the AgriAqua Research and Technology Center located at Nanga, Pototan, Iloilo, Philippines.A system estimated the weight of live tilapia, and images were captured in a free-swimming aquarium. The study established the relationship between the fish's shape and its mass in order to estimate the weight of the fish. The dataset used in the system is composed of several tilapia images acquired from specimens with weights ranging from approximately 120-250 grams. As part of the evaluation, this study determined the accuracy of three regression models: Linear, Multiple, and Polynomial regressions in estimating the weight of a freely-swimming Tilapia. In addition to the R2 and P-value, the models were also compared in terms of RMSE, MAE, MARE, MXAE, and MXRE. The system’s quality was also evaluated according to the standards for computer software set by ISO 25010 International Quality Standards
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
