Paulo César Pereira Júnior, Alexandre Monteiro, Rafael Da Luz Ribeiro, A. Sobieranski, A. V. Wangenheim
{"title":"经典计算机视觉与卷积神经网络在航空图像杂草映射中的比较","authors":"Paulo César Pereira Júnior, Alexandre Monteiro, Rafael Da Luz Ribeiro, A. Sobieranski, A. V. Wangenheim","doi":"10.22456/2175-2745.97835","DOIUrl":null,"url":null,"abstract":"In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images\",\"authors\":\"Paulo César Pereira Júnior, Alexandre Monteiro, Rafael Da Luz Ribeiro, A. Sobieranski, A. V. Wangenheim\",\"doi\":\"10.22456/2175-2745.97835\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.\",\"PeriodicalId\":82472,\"journal\":{\"name\":\"Research initiative, treatment action : RITA\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research initiative, treatment action : RITA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22456/2175-2745.97835\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research initiative, treatment action : RITA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22456/2175-2745.97835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images
In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.