检索结果-南通市图书馆

Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery

引用

International Journal of Agricultural and Biological Engineering 2019年第1期12卷 165-171页

作者： Jinru Xue Yeman Fan Baofeng Su sigfredo fuentes College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services Yangling 712100ShaanxiChina School of Agriculture and Food Faculty of Veterinary and Agricultural SciencesUniversity of MelbourneParkville 3010.VictoriaAustralia

Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant *** implementing a Digital Surface Model(DSM)to imagery obtained using Unmanned Aerial Vehicles(UAV),it is possible to filter canopy information effectively based on height,which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover *** paper describes a method based on the DSM to assess canopy growth(CG)as well as missing plants from a kiwifruit orchard on a plant-by-plant *** DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion(SfM)*** adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular ***,a customized algorithm was developed to discriminate single kiwifruit plants automatically,which allowed the estimation of individual canopy cover fractions(fc).By applying differential fc thresholding,four categories of the CG were determined automatically:(i)missing plants;(ii)low vigor;(iii)moderate vigor;and(iv)*** were validated by a detailed visual inspection on the ground,which rendered an overall accuracy of 89.5%for the method proposed to assess CG at the plant-by-plant ***,the accuracies for CG category(i)-(iv)were 94.1%,85.1%,86.7%,and 88.0%,*** proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to be distinguished by common colour thresholding or edge identification methods.

关键词： canopy vigor UAV imagery digital surface model kiwifruit plant missing plants photogrammetry plant stress

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies

引用

International Journal of Agricultural and Biological Engineering 2016年第6期9卷 119-130页

作者： Su Baofeng Xue Jinru Xie Chunyu Fang Yulin Song Yuyang sigfredo fuentes College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100China College of Enology Northwest A&F UniversityYangling 712100China Faculty of Veterinary and Agricultural Sciences University of MelbourneParkville 3010Australia

Accurate data acquisition and analysis to obtain crop canopy information are critical steps to understand plant growth dynamics and to assess the potential impacts of biotic or abiotic stresses on plant development.A versatile and easy to use monitoring system will allow researchers and growers to improve the follow-up management strategies within farms once potential problems have been *** study reviewed existing remote sensing platforms and relevant information applied to crops and specifically grapevines to equip a simple Unmanned Aerial Vehicle(UAV)using a visible high definition RGB *** objective of the proposed Unmanned Aerial System(UAS)was to implement a Digital Surface Model(DSM)in order to obtain accurate information about the affected or missing grapevines that can be attributed to potential biotic or abiotic stress *** analysis process started with a three-dimensional(3D)reconstruction from the RGB images collected from grapevines using the UAS and the Structure from Motion(SfM)technique to obtain the DSM applied on a per-plant ***,the DSM was expressed as greyscale images according to the halftone technique to finally extract the information of affected and missing grapevines using computer vision algorithms based on canopy cover measurement and *** validate the automated method proposed,each grapevine row was visually inspected within the study *** inspection was then compared to the digital assessment using the proposed UAS in order to validate calculations of affected and missing grapevines for the whole studied *** showed that the percentage of affected and missing grapevines was 9.5%and 7.3%,respectively from the area ***,for this specific study,the abiotic stress that affected the experimental vineyard(frost)impacted a total of 16.8%of *** study provided a new method for automatically surveying affected or missing grapevines in the field and an evaluation tool fo

关键词： remote sensing canopy cover viticultural management frost damage digital surface model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry,fractal dimension and visible/near infrared spectroscopy

引用

International Journal of Agricultural and Biological Engineering 2019年第2期12卷 123-131页

作者： Jinru Xue sigfredo fuentes Carlos Poblete-Echeverria Claudia Gonzalez Viejo Eden Tongson Hejuan Du Baofeng Su College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina School of Agriculture and Food.Faculty of Veterinary and Agricultural Sciences the University of Melbourne3010 Australia Faculty of AgriSciences Stellenbosch UniversityStellenbosch 7602South Africa Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Key Laboratory of Agricultural Perception and Intelligent Services Shaanxi ProvinceYangling 712100ShaanxiChina College of Information Engineering Tibet Nationality UniversityXianyang 712089ShaanxiChina

The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by *** recently,machine learning algorithms for pattern recognition have been successfully applied to leaf recognition in other plant *** new tools make the classification of Chinese medicinal plants easier,more efficient and cost *** study showed comparative results between machine learning models obtained from two methods:i)a morpho-colorimetric method and ii)a visible(VIS)/Near Infrared(NIR)spectral analysis from sampled leaves of 20 different Chinese medicinal ***,the automated image analysis and VIS/NIR spectral based parameters obtained from leaves were used separately as inputs to construct customized artificial neural network(ANN)*** showed that the ANN model developed using the morpho-colorimetric parameters as inputs(Model A)had an accuracy of 98.3%in the classification of leaves for the 20 medicinal plants *** the case of the model based on spectral data from leaves(Model B),the ANN model obtained using the averaged VIS/NIR spectra per leaf as inputs showed 92.5%accuracy for the classification of all medicinal plants *** A has the advantage of being cost effective,requiring only a normal document scanner as measuring *** method can be adapted for non-destructive assessment of leaves in-situ by using portable wireless *** B combines the fast,non-destructive advantages of VIS/NIR spectroscopy,which can be used for rapid and non-invasive identification of Chinese medicinal plants and other applications by analyzing specific light spectra overtones from leaves to assess concentration of pigments such as chlorophyll,anthocyanins and others that are related active compounds from the medicinal plants.

关键词： ampelography computer vision artificial neural networks pattern recognition Chinese medicinal plants

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

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：