Thales Santos Verne, Francisco Assis da Silva, Leandro Luiz de Almeida, Danillo Roberto Pereira, A. O. Artero
{"title":"DETECÇÃO E RECONHECIMENTO DE PLANTAS DE PEQUENO PORTE UTILIZANDO APRENDIZAGEM DE MÁQUINA","authors":"Thales Santos Verne, Francisco Assis da Silva, Leandro Luiz de Almeida, Danillo Roberto Pereira, A. O. Artero","doi":"10.5747/ce.2022.v14.n1.e383","DOIUrl":null,"url":null,"abstract":"The detection and recognition of plants has always been a difficult task even for connoisseurs and scholars due to the wide variety of plants found worldwide. With the advancement of technology, it has become possible to solve this problem computationally. This paper presents a method to perform plant detection and recognition from images using computer vision and artificial intelligence algorithms. The results show that the computational cost and recognition rate were satisfactory for use in controlled environments. The processing time to recognize each plant was 375 milliseconds, with an accuracy of 92%.","PeriodicalId":30414,"journal":{"name":"Colloquium Exactarum","volume":"22 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Colloquium Exactarum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5747/ce.2022.v14.n1.e383","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 detection and recognition of plants has always been a difficult task even for connoisseurs and scholars due to the wide variety of plants found worldwide. With the advancement of technology, it has become possible to solve this problem computationally. This paper presents a method to perform plant detection and recognition from images using computer vision and artificial intelligence algorithms. The results show that the computational cost and recognition rate were satisfactory for use in controlled environments. The processing time to recognize each plant was 375 milliseconds, with an accuracy of 92%.
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