Letícia Leite Caetano, Jocival Dantas Dias Júnior, A. Backes, H. Ferraz, M. Escarpinati
{"title":"Optimization of Management Zones Shape Files","authors":"Letícia Leite Caetano, Jocival Dantas Dias Júnior, A. Backes, H. Ferraz, M. Escarpinati","doi":"10.5753/wvc.2021.18905","DOIUrl":null,"url":null,"abstract":"The growing use of technologies in favor of Precision Agriculture enables the application of different strategies in a crop and seeks to increase production, reduce costs and reduce damage to the environment. To keep up with the need to increase productivity and still reduce costs with farming as much as possible, the approach of applying inputs in a targeted manner based on the classification of regions is increasingly used, as are the results obtained in [9]. In optimizing these results, some points were identified that could be improved in relation to the vector data of the generated Management Zones, such as overlapping between different zones, invalid geometries, and a very large amount of points, which add unnecessary complexity to the file. This work proposes an algorithm that aims to optimize these Management Zone results in a shapefile, and aims to correct invalid geometries, reduce the number of points that define the shapes of the zones, and the correction of overlapping regions so that zones with lesser vigor have priority. In addition, an adjustment of the spacing between the geometries is made while correcting the overlap between different zones. As a result, a new shapefile is created, composed only of valid geometries, fewer points, and no overlaps between different Management Zones. Specialists evaluated the results obtained and indicated them as adequate to solve the problem.","PeriodicalId":311431,"journal":{"name":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais do XVII Workshop de Visão Computacional (WVC 2021)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/wvc.2021.18905","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 growing use of technologies in favor of Precision Agriculture enables the application of different strategies in a crop and seeks to increase production, reduce costs and reduce damage to the environment. To keep up with the need to increase productivity and still reduce costs with farming as much as possible, the approach of applying inputs in a targeted manner based on the classification of regions is increasingly used, as are the results obtained in [9]. In optimizing these results, some points were identified that could be improved in relation to the vector data of the generated Management Zones, such as overlapping between different zones, invalid geometries, and a very large amount of points, which add unnecessary complexity to the file. This work proposes an algorithm that aims to optimize these Management Zone results in a shapefile, and aims to correct invalid geometries, reduce the number of points that define the shapes of the zones, and the correction of overlapping regions so that zones with lesser vigor have priority. In addition, an adjustment of the spacing between the geometries is made while correcting the overlap between different zones. As a result, a new shapefile is created, composed only of valid geometries, fewer points, and no overlaps between different Management Zones. Specialists evaluated the results obtained and indicated them as adequate to solve the problem.