检索结果-南通市图书馆

Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil

引用

Plant Phenomics 2020年第1期2卷 378-388页

作者： Afef Marzougui Yu Ma Rebecca J.McGee Lav R.Khot sindhuja sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWAUSA Department of Horticulture Washington State UniversityPullmanWAUSA United States Department of Agriculture-Agricultural Research Service Grain Legume Genetics and Physiology Research UnitWashington State UniversityPullmanWAUSA

Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping *** utilization of such technologies has enabled the generation of multidimensional plant traits creating big ***,to harness the power of phenomics technologies,more sophisticated data analysis methods are *** this study,Aphanomyces root rot(ARR)resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue(RGB)images of *** created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease *** approaches,generalized linear model with elastic net regularization(EN)and convolutional neural network(CNN),were developed to classify disease resistance categories into three classes:resistant,partially resistant,and *** results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0:91±0:004(0:96±0:005 resistant,0:82±0:009 partially resistant,and 0:92±0:007 susceptible)compared to CNN with an accuracy of about 0:84±0:009(0:96±0:008 resistant,0:68±0:026 partially resistant,and 0:83±0:015 susceptible).The resistant class was accurately detected using both classification ***,partially resistant class was challenging to detect as the features(data)of the partially resistant class often overlapped with those of resistant and susceptible ***,the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.

关键词： classify Elastic roots

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

Non-invasive sensing techniques to phenotype multiple apple tree architectures

引用

Information Processing in Agriculture 2023年第1期10卷 136-147页

作者： Chongyuan Zhang Sara Serra Juan Quiro´s-Vargas Worasit Sangjan Stefano Musacchi sindhuja sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWA 99164USA Tree Fruit Research&Extension Center Washington State UniversityWenatcheeWA 98801USA Department of Horticulture Washington State UniversityPullmanWA 99164USA

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development ***,the tree fruit architecture contributes to the light interception and improves tree growth,fruit quality,and fruit yield,in addition to easing the process of orchard management and *** tree architectural traits are measured manually by researchers or growers,which is labor-intensive and *** this study,the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders,physiologists and growers in collecting architectural traits efficiently and in a standardized *** this,a consumer-grade red–green–blue(RGB)camera was used to collect apple tree side-images,while an unmanned aerial vehicle(UAV)integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit *** sensing data were compared to ground reference data associated with tree orchard blocks within three training systems(Spindle,V-trellis,Biaxis),two rootstocks(‘WA 38 trees grafted on G41 and M9-Nic29)and two pruning methods(referred as bending and click pruning).The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface *** traits extracted from sensing data included box-counting fractal dimension(DBs),middle branch angle,number of branches,trunk basal diameter,and tree row volume(TRV).The results from ground-based sensing data indicated that there was a significant(P<0.0001)difference in DBs between Spindle and V-trellis training systems,and correlations between DBs with tree height(r=0.79)and total fruit yield per unit area(r=0.74)were significant(P<0.05).Moreover,correlations between average or total TRV and ground reference data,such as tree height and total fruit yield per unit area,were significant(P<0.05).With the r

关键词： Tree training systems Tree row volume Unmanned aerial vehicle Image analysis

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

High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management

引用

Information Processing in Agriculture 2022年第1期9卷 11-23页

作者： Rajeev Sinha Juan J.Quiros sindhuja sankaran Lav R.Khot Department of Biological Systems Engineering Center for Precision and Automated Agricultural SystemsWashington State UniversityProsserWA 99350United States

Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical prescription application ***,the existing ground based canopy sensing and attribute estimation methods are laborious and often involve complexity with field data collection and *** methods can be subjec-tive as ***,this study explores aerial photogrammetry based method of tree–row–volume(TRV),leaf–wall–area(LWA),canopy volume(CV)and canopy cover(CC)esti-mation for grapevine and apple *** sensing data was collected using a con-sumer–grade small unmanned aerial system(UAS)with an RGB imaging sensor flying at different flight altitudes i.e.,15 m(Ground sampling distance,GSD=0.45 cm pixel^(-1) at 65°sensor inclination),30 m(0.90 and 0.85 cm pixel^(-1) at 65°and 75°,respectively),45 m(1.35 and 1.27 cm pixel^(-1) at 65°and 75°,respectively)and 60 m(1.81 and 1.69 cm pixel^(-1) at 65°and 75°,respectively).Crop surface model(CSM)was derived from such data to esti-mate canopy height,width and foliage vigor,which are further used to estimate TRV,LWA,CV and *** ground measured and aerial imagery estimated TRV had a strong relation-ship with the data collected at the lowest GSD within grapevine canopies(R^(2)=0.77 at 0.45 cm pixel^(-1)as well as for apple canopies(R^(2)=0.82 at 0.90 cm pixel^(-1).Similar trends were observed for the LWA(R^(2)=0.77 and 0.86),CV(R^(2)=0.43 and 0.64)and CC(R^(2)=0.61 and 0.68)estimates for grapevine and apple canopies,*** GSD(≥0.45 cm pixel^(-1) in grapevine and≥0.90 cm pixel^(-1) in apple)resulted in a weak relationship between ground measurements and aerial imagery data-based estimates for grapevines(R^(2)=0.36)and apple canopies(R^(2)=0.39–0.78).Overall,the aerial flights with lower GSD and double grid missions with RGB imaging sensor in 65°orientation aided in the develo

关键词： Unmanned aerial system Photogrammetry Tree-row-volume Leaf-wall-area Canopy cover Prescription maps

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

Evaluation of biogenic markers-based phenotyping for resistance to Aphanomyces root rot in field pea

引用

Information Processing in Agriculture 2022年第1期9卷 1-10页

作者： Afef Marzougui Abirami Rajendran D.Scott Mattinson Yu Ma Rebecca J.McGee Manuel Garcia-Perez Stephen P.Ficklin sindhuja sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWAUnited States Department of Horticulture Washington State UniversityPullmanWAUnited States United States Department of Agriculture-Agricultural Research Service Grain Legume Genetics and Physiology Research UnitWashington State UniversityPullmanWAUnited States

Biogenic volatile organic compounds(VOCs)emitted by plants can reveal information about plant adaptation,defense processes,and biological ***,such VOC data may be utilized to capture phenotypic plant responses to the *** this study,the main objective was to evaluate the potential of biogenic compounds,including VOCs,to phenotype two pea cultivars,Ariel(susceptible)and Hampton(high levels of partial resistance)for resistance to Aphanomyces root rot *** were monitored non-destructively for VOC emission at three-time points(15,20,and 30 days after inoculation,DAI)using dynamic headspace sampling with gas chromatography-flame ionization detec-tion(GC-FID)system,as well as destructively at the end of the experiments,using solvent extraction and pyrolysis of both shoot and root tissues.A non-inoculated control(mock-inoculated with distilled water)was utilized to compare the plant responses within a *** common chemical peaks between control and inoculated samples of both culti-vars(RT_(cm))were analyzed after normalizing the relative peak intensity of inoculated samples with those of control samples,prior to a comparison between *** addi-tion,unique chemical peaks(RT_(uq))present in inoculated samples,but not in control sam-ples were also identified and their relative peak intensities were *** the released green leaf volatiles(RT_(cm)),the normalized relative peak intensity of hexanal emis-sion,at 20 DAI,was higher in Ariel than that of *** addition,several putative chemical peaks(both RT_(cm) and RT_(uq)),previously known as indicators for disease response,exhibited some differences in their emission rates between pea cultivars in at least one of the time *** destructive sampling revealed that shoot samples produced more putative unique biomarkers(RT_(uq))than the root *** on the differences in puta-tive chemical peaks between cultivars,this initial study supports the concept of utilizat

关键词： Plant breeding Pulse crop Volatile organic compounds Plant phenotyping Biotic 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

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

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

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：