H. M. Tran, K. T. Pham, Thanh M. Vo, L. T. That, T. T. M. Huynh, S. Dao
{"title":"Physical Characteristics Estimation for Irregularly Shaped Fruit Using Two Cameras","authors":"H. M. Tran, K. T. Pham, Thanh M. Vo, L. T. That, T. T. M. Huynh, S. Dao","doi":"10.1109/SSP53291.2023.10207992","DOIUrl":null,"url":null,"abstract":"The physical characteristics of agricultural products are crucial for developing grading, sizing, and packaging systems. So that, accurately measuring irregularly shaped products like starfruit is a challenging task. This paper proposes a technique that two cameras are used to estimate the dimensions, volume, and mass of starfruit with high accuracy. Firstly, top-view and body-view images of the starfruit are captured, and image processing techniques, conical frustum method are employed to find the volume based on the area ratio of star shape area over its bounding box and volume of multiple pieces along the longitudinal axis. Then, the density of the starfruit is used to estimate its mass. The proposed method has been validated with a highest average accuracy of 99.16% for the volume and 98.59% mass using 255 training samples. This technology is simple to adopt in starfruit and other fruit manufacturing lines","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP53291.2023.10207992","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The physical characteristics of agricultural products are crucial for developing grading, sizing, and packaging systems. So that, accurately measuring irregularly shaped products like starfruit is a challenging task. This paper proposes a technique that two cameras are used to estimate the dimensions, volume, and mass of starfruit with high accuracy. Firstly, top-view and body-view images of the starfruit are captured, and image processing techniques, conical frustum method are employed to find the volume based on the area ratio of star shape area over its bounding box and volume of multiple pieces along the longitudinal axis. Then, the density of the starfruit is used to estimate its mass. The proposed method has been validated with a highest average accuracy of 99.16% for the volume and 98.59% mass using 255 training samples. This technology is simple to adopt in starfruit and other fruit manufacturing lines