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[学术文献] A survey of the applications of text mining for agriculture 进入全文

Computers and Electronics in Agriculture

Agricultural researchers, in common with other domains, have recently began to have access to large collections of agricultural texts such as scientific papers and news stories. These texts can be analysed with text mining techniques to resolve agricultural problems or extract knowledge. Despite the potential of these techniques, text mining is a relatively underused technique in the agricultural domain. Therefore, this survey is intended to provide a current state of the art survey of the application of text mining techniques to agricultural problems.

[前沿资讯] 拜耳在阿根廷启动数字化农业平台FieldView™的商业推广 进入全文

世界农化网

针对即将到来的种植季,产业领先的数字化农业平台Climate FieldView已在阿根廷开始启动商业推广。FieldView™已经在全球范围内广泛采用,并通过数据驱动的数字工具改变着农民的生产管理模式。该平台将在下个种植季引入阿根廷,帮助农民获得最高的单位面积产量。FieldView由拜耳的子公司、农业数字创新领先者The Climate Corporation开发并提供,是目前市面上最完善的数字化农业平台。该平台提供易于使用的数据收集及存储工具,帮助农民更好地了解他们的农田全年情况,并快速、有效地优化管理决策,还有利于在发挥产量潜能的同时尽可能减少作物投入品的使用。

[科技报告] USDA Research is Driving Technological Innovation 进入全文

USDA

美国农业部(USDA)发布了年度技术转让报告,揭示了2018财年美国农业部实验室320项新发明,417项许可,120项申请专利和67项实际专利。这份报告突显了能为农民、农场主、林业工作者和从业者解决实际问题的科技创新,并为美国企业发展创造机会。

[前沿资讯] 韩国发布数据与人工智能经济激活计划 进入全文

中国科学院科技战略咨询研究院

韩国科学技术信息通信部与相关部门举办了第1次创新发展战略会议,制定并发布了“数据与人工智能经济激活计划”[1]。韩国政府自2018年8月宣布转向数据经济后,完善了数据激活的相关措施,11月向国会提交了平衡个人信息利用与保护的《个人信息相关法》修正案,并制定此次计划作为修正案的后续措施“数据与人工智能经济激活计划”目的是促进数据与人工智能的融合,并作为韩国创新发展领域的培育战略,是从今年起实施激活数据价值链、构建世界水平的人工智能创新生态系统、促进数据与人工智能融合等工作的5年计划,希望进入全球人工智能领先国家。目标至2023年实现:①数据市场投资规模从842亿元提高至1805亿元人民币[2];②人工智能独角兽企业从0个增加至10个;③打造数据与人工智能融合集群,培养1万名专业人才。

[学术文献] Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications 进入全文

International Journal of Production Economics

The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.

[前沿资讯] Using artificial intelligence to better predict severe weather 进入全文

EurekAlert

When forecasting weather, meteorologists use a number of models and data sources to track shapes and movements of clouds that could indicate severe storms. However, with increasingly expanding weather data sets and looming deadlines, it is nearly impossible for them to monitor all storm formations -- especially smaller-scale ones -- in real time. Now, there is a computer model that can help forecasters recognize potential severe storms more quickly and accurately, thanks to a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain. They have developed a framework based on machine learning linear classifiers -- a kind of artificial intelligence -- that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed. This AI solution ran on the Bridges supercomputer at the Pittsburgh Supercomputing Center.

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