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[学术文献] Machine learning and data analytics for the IoT 进入全文

Neural Computing and Applications

The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

[前沿资讯] Release of a new soil moisture product (2002-2011) for mainland China 进入全文

EurekAlert

作为关键气候变量(ECV)之一,土壤水分在水和能量循环以及陆气相互作用中扮演重要角色。尽管全球多个被动微波卫星将土壤水分作为其反演的主要目标,但由于植被和地表粗糙度的影响,在区域尺度上由卫星遥感获取高精度的土壤水分产品仍存在诸多挑战。在陆面过程模型中,同化被动微波信号是减小土壤水分估计偏差的有效方法,但同样面临气象驱动数据误差、模型参数的不确定性以及如何有效验证土壤水分精度等问题。 过去十余年间,清华大学和中科院青藏高原研究所研究人员开发了可优化模型参数的双通道数据同化系统,发展了可供驱动陆面数据同化系统的中国区域高时空分辨率气象要素数据集,并与其他团队共同建立了包含逾100个站点的四个土壤水分观测网作为验证基地,最终通过同化被动微波卫星AMSR-E亮温生成了2002-2011年中国格点尺度土壤水分数据集。基于土壤水分观测网的评估结果,表明其相比于卫星反演产品和陆面过程模拟具有更高的精度。该数据集空间分辨率为0.25°,包括逐日的表层、根部层和深层的土壤水分含量。该数据已在国家青藏高原科学数据中心在线发布,并被用于陆气相互作用、冻土退化以及高寒生态等研究。

[学术文献] A storage architecture for high-throughput crop breeding data based on improved blockchain technology 进入全文

Computers and Electronics in Agriculture

This paper presents an architecture to store high-throughput crop breeding data efficiently and safely, by using improved blockchain technology in a breeding information management system, Golden Seed Breeding Cloud Platform (GSBCP). The storage architecture was devised to ensure (a) efficient and safe storage of breeding data, (b) easy and quick acquisition of breeding data from the dimensions of crop planting location and breeding process, (c) easy extension of the architecture to improve system performance as data volume grows, and (d) low cost of system hardware. This storage architecture uses a light blockchain to save key breeding data. Different types of blockchains are used to store different types of breeding data. Proxy encryption technology is used to guarantee data security. To strike a balance between data security and system performance, important data are assigned a higher level of security. This storage architecture can significantly improve the efficiency of GSBCP when the amount of data is large, especially when breeders query breeding data. In addition, increasing the number of blockchain nodes can greatly improve the concurrency of the system.

[学术文献] Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme 进入全文

Agricultural Systems

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of ‘big data’ use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement.  

[学术文献] Smart poultry management: Smart sensors, big data, and the internet of things 进入全文

Computers and Electronics in Agriculture

As the world’s population increases, demand for poultry products will continue to increase. To meet this demand, one candidate mechanism to increase production is to increase housing and manage more birds. However, this practice, along with labour shortages and increasing biosecurity practices will make it increasingly difficult for producers to monitor the production, health, and welfare status of all their birds. Employing smart poultry management systems is necessary to increase production while minimizing costs and the use of resources. Smart poultry management systems include precision livestock farming (PLF) technologies such as smart sensors, automation of farm processes, and data driven decision making platforms. Many new technologies will have great implications for poultry production in the areas of the poultry house environment, bird welfare, precision feeding, and rapid detection of infectious disease. As smart sensors collect data in real-time on a variety of parameters from poultry operations, large amounts of data will be generated. To make best use of this data, big data analytical tools must be employed to produce data driven decisions. Additionally, the devices that will be incorporated into smart poultry management systems will be connected to the Internet allowing for the formation of Internet of things (IoT) farm networks. IoT technologies allow for communication between farm sensors, devices, and equipment, and will lead to the automation of multiple farm procedures. The following review discusses the areas of impact that new smart sensor technologies will have on poultry operations and describes how sensor technology is related to big data analytics and IoT systems, and how these technologies can enhance production in the poultry industry. Additionally, challenges to the described systems and technologies will also be highlighted and discussed.

[学术文献] Polly: A Tool for Rapid Data Integration and Analysis in Support of Agricultural Research and Education 进入全文

Internet of Things

Data analysis and modeling is a complex and demanding task. While a variety of software and tools exist to cope with this problem and tame big data operations, most of these tools are either not free, and when they are, they require large amount of configuration and steep learning curve. Moreover, they provide limited functionalities. In this paper we propose Polly, an online data analysis and modeling open-source tool that is intuitive to use and can be used with minimal or no configuration. Users can use Polly to rapidly integrate, analyze their data, prototype and test their novel methodologies. Polly can be used also as an educational tool. Users can use Polly to upload or connect to their structured data sources, load the required data into our system and perform various data processing tasks. Examples of such operations include data cleaning, data pre-processing, attribute encoding, regression and classification analysis. Aside from modeling, users can then download their results in the form of graphs in several standard visualization formats. While in this paper we focus on analyzing dataset for smart farming, our tool usage fits to a more general audience. To justify our backend design and implementation choices, we also present a performance analysis between backend virtualization technologies (containers or serverless computing), showing both expected and surprising results.

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