W. Jinyong, Tan Wenrong, Hou Shuaimin, Wang Yang, Zhang Hongna
{"title":"基于机器视觉的玉米叶片表型参数定量方法研究","authors":"W. Jinyong, Tan Wenrong, Hou Shuaimin, Wang Yang, Zhang Hongna","doi":"10.1109/ISCEIC51027.2020.00026","DOIUrl":null,"url":null,"abstract":"This paper aims to obtain a model (method) for monitering the growth of corn automatically and accurately. The latest computer vision technology is applied to calculate the method of extracting the surface characteristic parameters. Binocular vision technology is used to calculate the 3D point cloud of maize image. Meanwhile, the 3D point noise reduction established by bilateral filtering is adopted to establish the 3D reconstruction model of maize. In the 3D model, the height and width of maize are calculated proportionally. The error rate of the proposed method is 0.52%. This work provides a reference for the growth monitoring and virtual growth of corn.","PeriodicalId":249521,"journal":{"name":"2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Research on Quantification Method of Maize Leaf Phenotype Parameters Based on Machine Vision\",\"authors\":\"W. Jinyong, Tan Wenrong, Hou Shuaimin, Wang Yang, Zhang Hongna\",\"doi\":\"10.1109/ISCEIC51027.2020.00026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to obtain a model (method) for monitering the growth of corn automatically and accurately. The latest computer vision technology is applied to calculate the method of extracting the surface characteristic parameters. Binocular vision technology is used to calculate the 3D point cloud of maize image. Meanwhile, the 3D point noise reduction established by bilateral filtering is adopted to establish the 3D reconstruction model of maize. In the 3D model, the height and width of maize are calculated proportionally. The error rate of the proposed method is 0.52%. This work provides a reference for the growth monitoring and virtual growth of corn.\",\"PeriodicalId\":249521,\"journal\":{\"name\":\"2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"volume\":\"149 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCEIC51027.2020.00026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCEIC51027.2020.00026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Quantification Method of Maize Leaf Phenotype Parameters Based on Machine Vision
This paper aims to obtain a model (method) for monitering the growth of corn automatically and accurately. The latest computer vision technology is applied to calculate the method of extracting the surface characteristic parameters. Binocular vision technology is used to calculate the 3D point cloud of maize image. Meanwhile, the 3D point noise reduction established by bilateral filtering is adopted to establish the 3D reconstruction model of maize. In the 3D model, the height and width of maize are calculated proportionally. The error rate of the proposed method is 0.52%. This work provides a reference for the growth monitoring and virtual growth of corn.