The Use of Distributed Processing and Cloud Computing in Agricultural Decision-Making Support Systems

Goya, Walter Akio; Andrade, Marcelo Risse de; Zucchi, Artur Carvalho; Gonzalez, Nelson Mimura; Pereira, Rosangela de Fátima; Langona, Karen; Carvalho, Tereza Cristina de; Mångs, Jan-Erik; Sefidcon, Azimeh

doi:10.1109/cloud.2014.101

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Nowadays, IoT has become one of the most promising technologies to deal with modern "smart" life systems, organizations, environments, industries, and farms [35]. IoT is heavily present in agriculture management processes and is deployed to increase productivity [36].…”

Section: Data Sources and Low Power Consumption Embedded Devicesmentioning

confidence: 99%

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

2022

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Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights.

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Section: Data Sources and Low Power Consumption Embedded Devicesmentioning

confidence: 99%

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

2022

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“…The analytics and decision support is based on a conceptual model that considers various inputs, such as a plant evolution model and environmental factors, historical data sets, etc. [25]. The plant evolution is a holistic topic that involves factors from multiple domains.…”

Section: Data Analytics Approachmentioning

confidence: 99%

Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture

Bălan¹,

Dumitru²,

Dudnik

et al. 2020

Sensors

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Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world’s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.

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“…Goya et al [5] described the decision making for supporting agriculture with a cloud computing platform, distributed processing, and technology in the framework of big data. Weather data do the metric calculation, and the solution to test in the different virtual machine scenario, and configuration like a cluster in helping the farmer to get a better prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Description Architecture/Methodology Benefits Goya et al [5] A decision-making system for supporting agriculture with cloud, processing technology in big data…”

Section: Sno Authormentioning

confidence: 99%

A Big Data Virtualization Role in Agriculture: A Comprehensive Review

MATHIVANAN¹,

Jayagopal²

2018

Walailak J Sci & Tech

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Big data is a collection of large volumes of data sets which are more complicated to analyze using standard data processing methods. It also emphasizes parameters like data variety and velocity data. Big data will play a most significant role in our daily life regarding applications like healthcare electronic commerce, agriculture, telecommunication, government, and financial trading. In the agriculture domain, big data is an optimal method to increase the productivity of farming by gathering and processing information like plant growth, farmland monitoring, greenhouse gases monitoring, climate change, soil monitoring and so forth. Virtualization is an emerging technique that can be combined with big data in agriculture. Virtualization has been used extensively in research for a long time, the term “virtual” entities affecting a real-life form. In agriculture, it has many more physical objects, sensors, and devices. This physical object is virtualized and has digital representation to store, communicate and process via the internet. The information from the virtual object has a large volume of data which helps meaningful data analysis or aspects to make application services like decision making, problem notification, and information handling. This paper provides a comprehensive review of big data virtualization in the agriculture domain. The virtualization methodology, and tools used by many researchers is surveyed.

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The Use of Distributed Processing and Cloud Computing in Agricultural Decision-Making Support Systems

Cited by 8 publications

References 8 publications

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture

A Big Data Virtualization Role in Agriculture: A Comprehensive Review

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