种子图像分析对森林物种特征和分化的一种新方法

IF 0.4 4区 农林科学 Q4 FORESTRY Ciencia Florestal Pub Date : 2023-10-18 DOI:10.5902/1980509873427
Francival Cardoso Felix, Dagma Kratz, Richardson Ribeiro, Antonio Carlos Nogueira
{"title":"种子图像分析对森林物种特征和分化的一种新方法","authors":"Francival Cardoso Felix, Dagma Kratz, Richardson Ribeiro, Antonio Carlos Nogueira","doi":"10.5902/1980509873427","DOIUrl":null,"url":null,"abstract":"Biometric seed analysis can be used to characterize and differentiate forest species. However, forest species are generally studied using manual methods such as measurements with a digital caliper, which provides a limited amount of information on plant morphological characteristics, whereas agronomic species are analyzed using expensive and often inaccessible equipment. Thus, the objective of the present study was to demonstrate that seed image analysis and processing tools can help characterize and differentiate Brazilian forest species. For this purpose, the seeds of 155 forest species belonging to 42 families were photographed and analyzed to extract data on their morphometric descriptors using a new methodological approach. A total of 18 characteristics were assessed, namely eight dimensions, four shape characteristics, and six color characteristics. A set of approximately 1.827 million data was extracted from 101,521 seed images. Digital image processing efficiently characterized the studied seeds and the obtained characteristics allowed us to differentiate between species, including those belonging to the same botanical family and genus. Therefore, seed image analysis using the proposed methodology can be used to characterize, differentiate, and automatedly identify forest species in Brazil.","PeriodicalId":10244,"journal":{"name":"Ciencia Florestal","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterization and differentiation of forest species by seed image analysis: a new methodological approach\",\"authors\":\"Francival Cardoso Felix, Dagma Kratz, Richardson Ribeiro, Antonio Carlos Nogueira\",\"doi\":\"10.5902/1980509873427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biometric seed analysis can be used to characterize and differentiate forest species. However, forest species are generally studied using manual methods such as measurements with a digital caliper, which provides a limited amount of information on plant morphological characteristics, whereas agronomic species are analyzed using expensive and often inaccessible equipment. Thus, the objective of the present study was to demonstrate that seed image analysis and processing tools can help characterize and differentiate Brazilian forest species. For this purpose, the seeds of 155 forest species belonging to 42 families were photographed and analyzed to extract data on their morphometric descriptors using a new methodological approach. A total of 18 characteristics were assessed, namely eight dimensions, four shape characteristics, and six color characteristics. A set of approximately 1.827 million data was extracted from 101,521 seed images. Digital image processing efficiently characterized the studied seeds and the obtained characteristics allowed us to differentiate between species, including those belonging to the same botanical family and genus. Therefore, seed image analysis using the proposed methodology can be used to characterize, differentiate, and automatedly identify forest species in Brazil.\",\"PeriodicalId\":10244,\"journal\":{\"name\":\"Ciencia Florestal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ciencia Florestal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5902/1980509873427\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ciencia Florestal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5902/1980509873427","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0

摘要

种子生物特征分析可用于森林物种的特征和区分。然而,研究森林物种通常使用人工方法,例如使用数字卡尺测量,这提供了有限数量的植物形态特征信息,而分析农艺物种则使用昂贵且往往难以获得的设备。因此,本研究的目的是证明种子图像分析和处理工具可以帮助表征和区分巴西森林物种。为此,采用一种新的方法对42科155种森林物种的种子进行了拍摄和分析,提取了它们的形态计量描述符数据。总共评估了18个特征,即8个维度,4个形状特征和6个颜色特征。从101521张种子图像中提取了大约182.7万组数据。数字图像处理有效地表征了所研究的种子,所获得的特征使我们能够区分物种,包括那些属于同一植物科和属的物种。因此,采用该方法的种子图像分析可用于巴西森林物种的表征、区分和自动识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Characterization and differentiation of forest species by seed image analysis: a new methodological approach
Biometric seed analysis can be used to characterize and differentiate forest species. However, forest species are generally studied using manual methods such as measurements with a digital caliper, which provides a limited amount of information on plant morphological characteristics, whereas agronomic species are analyzed using expensive and often inaccessible equipment. Thus, the objective of the present study was to demonstrate that seed image analysis and processing tools can help characterize and differentiate Brazilian forest species. For this purpose, the seeds of 155 forest species belonging to 42 families were photographed and analyzed to extract data on their morphometric descriptors using a new methodological approach. A total of 18 characteristics were assessed, namely eight dimensions, four shape characteristics, and six color characteristics. A set of approximately 1.827 million data was extracted from 101,521 seed images. Digital image processing efficiently characterized the studied seeds and the obtained characteristics allowed us to differentiate between species, including those belonging to the same botanical family and genus. Therefore, seed image analysis using the proposed methodology can be used to characterize, differentiate, and automatedly identify forest species in Brazil.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ciencia Florestal
Ciencia Florestal 农林科学-林学
CiteScore
0.80
自引率
0.00%
发文量
85
审稿时长
18-36 weeks
期刊介绍: The journal Forest Science was established in 1991 with the goal of being a vehicle for dissemination which are published works tércnico-scientific forest-related, the following bodies crowded the Centro de Ciências Rurais of Universidade Federal de Santa Maria: - Centro de Pesquisas Florestais - CEPEF - Programa de Pós-graduação em Engenharia Florestal - PPGEF - Departamento de Ciências Florestais - DCFL MISSION: Publish scientific papers, technical notes, and literature reviews related to the area of ​​forest sciences.
期刊最新文献
Propriedades físico-mecânicas de painéis compensados com a madeira de <i>Cupressus lusitanica</i> Mill. Aspectos iniciais da fenologia reprodutiva de plantas de <i>Monteverdia ilicifolia</i> (Mart. ex. Reissek) Biral cultivadas com bioestimulantes Comportamento do fogo em espécies nativas da Caatinga na região geográfica imediata de Patos-PB Resposta morfofisiológica de plantas do Cerrado à aplicação de biochar de torta de filtro <i>Trichoderma asperellum</i> no controle de cancro do tronco em <i>Carya illinoinensis</i>
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1