大数据、物联网和人工智能等现代信息技术在农业中的广泛应用,推动了农业农村现代化和智慧农业的发展,带动了农业经营主体对科技与知识的旺盛需求,农业知识服务成为农业转型升级和高质量发展的重要引擎。为解决现有农业知识分散无序、...大数据、物联网和人工智能等现代信息技术在农业中的广泛应用,推动了农业农村现代化和智慧农业的发展,带动了农业经营主体对科技与知识的旺盛需求,农业知识服务成为农业转型升级和高质量发展的重要引擎。为解决现有农业知识分散无序、更新不及时、面向经营主体的知识服务不平衡、供需脱节等问题,本文总结分析了国内外农业知识服务的研究与实践现状,提出了一套基于农业全产业链、按照农业数据的全生命周期、面向农业经营主体的农业智能知识服务体系框架,设计了基于智能物联网(Artificial Intelligence&Internet of Things,AIoT)的农情感知与大数据汇聚治理、基于知识图谱的农业知识组织与计算挖掘,以及基于多场景的农业智能知识服务三个层次。文中归纳了包括空天地AIoT全维度农情感知、多源异构农业大数据汇聚治理、知识建模、知识抽取、知识融合、知识推理、跨媒体检索、智能问答、个性化推荐技术、决策支持等农业智能知识服务涉及的关键技术,并举例了其研究应用。最后从农业数据获取、模型构建、知识组织、智能知识服务技术和应用推广等方面探讨了未来农业智能知识服务的发展趋势及对策建议。总结发现,农业智能知识服务是破解当前农业知识服务供需矛盾,实现跨媒体农业数据到知识的跨越,推动农业知识服务向个性化、精准化和智能化升级的关键,亦是农业科技自立自强、现代农业提质增效的重要支撑。展开更多
A number of basic and applied questions in ecology and environmental management require the characterization of soil and leaf litter faunal diversity. Recent advances in high-throughput sequencing of barcode-gene ampl...A number of basic and applied questions in ecology and environmental management require the characterization of soil and leaf litter faunal diversity. Recent advances in high-throughput sequencing of barcode-gene amplicons ('metabarcoding') have made it possible to survey biodiversity in a robust and efficient way. However, one obstacle to the widespread adoption of this technique is the need to choose amongst many candidates for bioinformatic processing of the raw sequencing data. We compare three candidate pipelines for the processing of 18S small subunit rDNA metabarcode data from solid substrates: (i) USEARCH/CROP, (ii) Denoiser/UCLUST, and (iii) OCTUPUS. The three pipelines produced reassuringly similar and highly correlated assessments of community composition that are dominated by taxa known to characterize the sampled environments. However, OCTUPUS appears to inflate phylogenetic diversity, because of higher sequence noise. We therefore recommend either the USEARCH/CROP or Denoiser/UCLUST pipelines, both of which can be run within the QIIME (Quantitative Insights Into Microbial Ecology) environment.展开更多
文摘大数据、物联网和人工智能等现代信息技术在农业中的广泛应用,推动了农业农村现代化和智慧农业的发展,带动了农业经营主体对科技与知识的旺盛需求,农业知识服务成为农业转型升级和高质量发展的重要引擎。为解决现有农业知识分散无序、更新不及时、面向经营主体的知识服务不平衡、供需脱节等问题,本文总结分析了国内外农业知识服务的研究与实践现状,提出了一套基于农业全产业链、按照农业数据的全生命周期、面向农业经营主体的农业智能知识服务体系框架,设计了基于智能物联网(Artificial Intelligence&Internet of Things,AIoT)的农情感知与大数据汇聚治理、基于知识图谱的农业知识组织与计算挖掘,以及基于多场景的农业智能知识服务三个层次。文中归纳了包括空天地AIoT全维度农情感知、多源异构农业大数据汇聚治理、知识建模、知识抽取、知识融合、知识推理、跨媒体检索、智能问答、个性化推荐技术、决策支持等农业智能知识服务涉及的关键技术,并举例了其研究应用。最后从农业数据获取、模型构建、知识组织、智能知识服务技术和应用推广等方面探讨了未来农业智能知识服务的发展趋势及对策建议。总结发现,农业智能知识服务是破解当前农业知识服务供需矛盾,实现跨媒体农业数据到知识的跨越,推动农业知识服务向个性化、精准化和智能化升级的关键,亦是农业科技自立自强、现代农业提质增效的重要支撑。
基金supported by Yunnan Province (20080A001)Chinese Academy of Sciences (0902281081,KSCX2-YW-Z-1027)+2 种基金the National Natural Science Foundation of China (31170498)Ministry of Science and Technology of China (2012FY110800)Kunming Institute of Zoology,and the University of East Anglia
文摘A number of basic and applied questions in ecology and environmental management require the characterization of soil and leaf litter faunal diversity. Recent advances in high-throughput sequencing of barcode-gene amplicons ('metabarcoding') have made it possible to survey biodiversity in a robust and efficient way. However, one obstacle to the widespread adoption of this technique is the need to choose amongst many candidates for bioinformatic processing of the raw sequencing data. We compare three candidate pipelines for the processing of 18S small subunit rDNA metabarcode data from solid substrates: (i) USEARCH/CROP, (ii) Denoiser/UCLUST, and (iii) OCTUPUS. The three pipelines produced reassuringly similar and highly correlated assessments of community composition that are dominated by taxa known to characterize the sampled environments. However, OCTUPUS appears to inflate phylogenetic diversity, because of higher sequence noise. We therefore recommend either the USEARCH/CROP or Denoiser/UCLUST pipelines, both of which can be run within the QIIME (Quantitative Insights Into Microbial Ecology) environment.
