Михаил Германович Катаев, M. Kataev, Кирилл Ёлгин, Kirill Yolgin
{"title":"Determination of Plant Phenological Cycle From RGB Images","authors":"Михаил Германович Катаев, M. Kataev, Кирилл Ёлгин, Kirill Yolgin","doi":"10.30987/graphicon-2019-2-178-181","DOIUrl":null,"url":null,"abstract":"Automated visual assessment of the state of the earth and plants, wilting and pests of leaves, plant growth indicators, using technical vision, can be used as a basis in smart (precision) agriculture (SA). This article discusses a brief review of the literature on the use of computer (technical) vision (CV) for analyzing the condition of agricultural fields and plants growing on them. The introduction of vision systems into real agricultural production practice is associated with the development of complex mathematical approaches that must be resistant to a variety of technical and weather changes. It is necessary to overcome image changes caused by atmospheric conditions and daily and seasonal variations in sunlight. An approach is proposed, which is based on an RGB image obtained using a typical digital camera. The results are given on the use of CV systems in solving individual tasks of agricultural production.","PeriodicalId":409819,"journal":{"name":"GraphiCon'2019 Proceedings. Volume 2","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GraphiCon'2019 Proceedings. Volume 2","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30987/graphicon-2019-2-178-181","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
Automated visual assessment of the state of the earth and plants, wilting and pests of leaves, plant growth indicators, using technical vision, can be used as a basis in smart (precision) agriculture (SA). This article discusses a brief review of the literature on the use of computer (technical) vision (CV) for analyzing the condition of agricultural fields and plants growing on them. The introduction of vision systems into real agricultural production practice is associated with the development of complex mathematical approaches that must be resistant to a variety of technical and weather changes. It is necessary to overcome image changes caused by atmospheric conditions and daily and seasonal variations in sunlight. An approach is proposed, which is based on an RGB image obtained using a typical digital camera. The results are given on the use of CV systems in solving individual tasks of agricultural production.