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[学术文献] Architecture design approach for IoT-based farm management information systems 进入全文

Precision Agriculture

Smart farming adopts advanced technology and the corresponding principles to increase the amount of production and economic returns, often also with the goal to reduce the impact on the environment. One of the key elements of smart farming is the farm management information systems (FMISs) that supports the automation of data acquisition and processing, monitoring, planning, decision making, documenting, and managing the farm operations. An increased number of FMISs now adopt internet of things (IoT) technology to further optimize the targeted business goals. Obviously IoT systems in agriculture typically have different functional and quality requirements such as choice of communication protocols, the data processing capacity, the security level, safety level, and time performance. For developing an IoT-based FMIS, it is important to design the proper architecture that meets the corresponding requirements. To guide the architect in designing the IoT based farm management information system that meets the business objectives a systematic approach is provided. To this end a design-driven research approach is adopted in which feature-driven domain analysis is used to model the various smart farming requirements. Further, based on a FMIS and IoT reference architectures the steps and the modeling approaches for designing IoT-based FMIS architectures are described. The approach is illustrated using two case studies on smart farming in Turkey, one for smart wheat production in Konya, and the other for smart green houses in Antalya.

[学术文献] Study of Wireless Communication Technologies on Internet of Things for Precision Agriculture 进入全文

Wireless Personal Communications

Precision agriculture is a suitable solution to these challenges such as shortage of food, deterioration of soil properties and water scarcity. The developments of modern information technologies and wireless communication technologies are the foundations for the realization of precision agriculture. This paper attempts to find suitable, feasible and practical wireless communication technologies for precision agriculture by analyzing the agricultural application scenarios and experimental tests. Three kinds of Wireless Sensor Networks (WSN) architecture, which is based on narrowband internet of things (NB-IoT), Long Range (LoRa) and ZigBee wireless communication technologies respectively, are presented for precision agriculture applications. The feasibility of three WSN architectures is verified by corresponding tests. By measuring the normal communication time, the power consumption of three wireless communication technologies is compared. Field tests and comprehensive analysis show that ZigBee is a better choice for monitoring facility agriculture, while LoRa and NB-IoT were identified as two suitable wireless communication technologies for field agriculture scenarios.

[前沿资讯] Using machine learning to understand climate change 进入全文

EurekAlert

Methane is a potent greenhouse gas that is being added to the atmosphere through both natural processes and human activities, such as energy production and agriculture. To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs--both natural and human--as well as the outputs. They also need to know how long methane resides in the atmosphere. To help develop this understanding, Tom Weber, an assistant professor of earth and environmental sciences at the University of Rochester; undergraduate researcher Nicola Wiseman '18, now a graduate student at the University of California, Irvine; and their colleague Annette Kock at the GEOMAR Helmholtz Centre for Ocean Research in Germany, used data science to determine how much methane is emitted from the ocean into the atmosphere each year. Their results, published in the journal Nature Communications, fill a longstanding gap in methane cycle research and will help climate scientists better assess the extent of human perturbations. The study is part of Weber's effort to use data science to better understand how various greenhouse gases, including nitrogen and carbon dioxide, affect global climate systems.

[前沿资讯] Machine learning helps plant science turn over a new leaf 进入全文

EurekAlert

Father of genetics Gregor Mendel spent years tediously observing and measuring pea plant traits by hand in the 1800s to uncover the basics of genetic inheritance. Today, botanists can track the traits, or phenotypes, of hundreds or thousands of plants much more quickly, with automated camera systems. Now, Salk researchers have helped speed up plant phenotyping even more, with machine-learning algorithms that teach a computer system to analyze three-dimensional shapes of the branches and leaves of a plant. The study, published in Plant Physiology on October 7, 2019, may help scientists better quantify how plants respond to climate change, genetic mutations or other factors.

[前沿资讯] Urban agriculture can push the sustainability 进入全文

ScienceDaily(美国)

A community garden occupies a diminutive dirt lot in Phoenix. Rows of raised garden beds offer up basil, watermelons and corn, making this patch of land an agricultural oasis in a desert city of 1.5 million people. In fact, this little garden is contributing in various ways to the city's environmental sustainability goals set by the city council in 2016. The goals consider matters such as transportation, water stewardship, air quality and food. With these goals in mind, a group of researchers led by Arizona State University assessed how urban agriculture can help Phoenix meet its sustainability goals. For example, urban agriculture could help eliminate so called "food deserts," communities that lack retail grocers. It also can provide green space, and energy and CO2 emissions savings from buildings.

[科技报告] Data Management Plan for NIFA-Funded Research, Education, and Extension Projects 进入全文

USDA

美国国家农业与食品研究院(NIFA)以投资并推动农业研究、教育与推广来解决社会挑战为使命,NIFA对转型科学的资金支持保证了美国农业的长期繁荣发展和全球领先地位。增加对NIFA资助项目科学研究成果(学术出版物、数字数据集)、教育(课程和培训产品)和推广数据的获取渠道对NIFA实现催化变革性发现、教育和参与以应对农业挑战的愿景至关重要。因此,给予提交到NIFA的项目适当的数据管理计划是研究、教育和推广活动的核心组成部分。在2019财年,NIFA将为接收资助的项目申请数字管理计划

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