检索结果-内蒙古大学图书馆

Stable information transfer network facilitates the emergence of collective behavior of bird flocks

Physical Review E 2018年第5期98卷 052309-052309页

作者： Jiaping Ren Wanxuan Sun Dinesh Manocha Aming Li Xiaogang Jin State Key Lab of CAD&CG Zhejiang University Hangzhou 310058 China Department of Computer Science and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Department of Zoology and Oxford Centre for Integrative Systems Biology University of Oxford Oxford OX1 3PS United Kingdom Chair of Systems Design ETH Zürich Weinbergstrasse 56/58 Zürich CH-8092 Switzerland

Collective behavior is ubiquitous in living systems such as human crowds, microbial communities, insect swarms, and bird flocks. It is widely reported that simple, local, temporal interactions between individuals may ... 详细信息

来源：评论

A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery

arXiv

引用

arXiv 2020年

作者： Osco, Lucas Prado dos Santos de Arruda, Mauro Gonçalves, Diogo Nunes Dias, Alexandre Batistoti, Juliana de Souza, Mauricio Gomes, Felipe David Georges Ramos, Ana Paula Marques de Castro Jorge, Lúcio André Liesenberg, Veraldo Li, Jonathan Ma, Lingfei Marcato, José Gonçalves, Wesley Nunes Faculty of Engineering and Architecture and Urbanism University of Western São Paulo R. José Bongiovanni 700 - Cidade Universitária Presidente PrudenteSP19050-920 Brazil Faculty of Computer Science Federal University of Mato Grosso do Sul Av. Costa e Silva s/n Campo GrandeMS79070-900 Brazil Faculty of Veterinary Medicine and Animal Science Federal University of Mato Grosso do Sul Avenida Senador Filinto Muller 2443 Campo GrandeMS79074-960 Brazil Faculty of Engineering Architecture and Urbanism and Geography Federal University of Mato Grosso do Sul Av. Costa e Silva Campo GrandeMS79070-900 Brazil Rodovia Raposo Tavares km 572 - Limoeiro Pres. PrudenteSP19067-175 Brazil R. XV de Novembro 1452 São CarlosSP13560-970 Brazil Av. Luiz de Camões 2090 Conta Dinheiro LagesSC88520-000 Brazil Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada

Accurately mapping croplands is an important prerequisite for precision farming since it assists in field management, yield-prediction, and environmental management. Crops are sensitive to planting patterns and some have a limited capacity to compensate for gaps within a row. Optical imaging with sensors mounted on Unmanned Aerial Vehicles (UAV) is a cost-effective option for capturing images covering croplands nowadays. However, visual inspection of such images can be a challenging and biased task, specifically for detecting plants and rows on a one-step basis. Thus, developing an architecture capable of simultaneously extracting plant individually and plantation-rows from UAV-images is yet an important demand to support the management of agricultural systems. In this paper, we propose a novel deep learning method based on a Convolutional Neural Network (CNN) that simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The experimental setup was evaluated in (a) a cornfield (Zea mays L.) with different growth stages (i.e. recently planted and mature plants) and in a (b) Citrus orchard (Citrus Sinensis Pera). Both datasets characterize different plant density scenarios, in different locations, with different types of crops, and from different sensors and dates. This scheme was used to prove the robustness of the proposed approach, allowing a broader discussion of the method. A two-branch architecture was implemented in our CNN method, where the information obtained within the plantation-row is updated into the plant detection branch and retro-feed to the row branch;which are then refined by a Multi-Stage Refinement method. In the corn plantation datasets (with both growth phases – young and mature), our approach returned a mean absolute error (MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038, precision and recall values of 0.856, and 0.905, respectively, and an F-m

来源：评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：