CLUeFARM: Integrated web-service platform for smart farms

Colezea, Madalin; Musat, George-Alexandru; Pop, Florin; Negru, Cătălin; Dumitrascu, Alexandru; Mocanu, Mariana

doi:10.1016/j.compag.2018.08.015

Cited by 47 publications

(17 citation statements)

References 10 publications

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“…In most cases, the common architectures described thus far in the literature focus on special applications from the domain of agriculture, such as pig breeding [42] or tomato cultivation [12]; however, many existing architectural approaches have a strong focus on hardware and technology [11,43], which in turn is limited to specific…”

Section: Information System Architecture For Smart Farmingmentioning

confidence: 99%

“…The data are stored both in local data store and within a cloud store [42,50]. The duality of data storage ensures data security on the one hand, and on the other hand, it enables analysis and access to mission-critical data even in the event of network failures [22,42,43,52]. Cloud-based storage reduces the management overhead for data storage and enables data access from any location [42].…”

Section: Information System Architecture For Smart Farmingmentioning

confidence: 99%

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Farming in the Era of Internet of Things: An Information System Architecture for Smart Farming

Strobel¹

2020

WI2020 Community Tracks

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The Internet of Things and associated smart products are finding application in evermore domains. Within agriculture it is described under the term smart farming. Using smart products allows farmers to automatically record relevant information, monitor operating procedures or remotely control machines. To make these capabilities usable for added value, not only smart products but entire information systems that align to the domain and its requirements are necessary. Within the literature, various architectures can be found already. However, many lack a methodical foundation or an abstraction of application cases and technology. Against this background, the article presents an information system architecture independent of application cases or technology and oriented toward the domain of smart farming. Starting point of the development is a systematic literature review, based on Webster and Watson [1] in combination with Vom Brocke [2, 3], based on 18 existing architecture approaches that are analyzed and aggregated.

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Section: Information System Architecture For Smart Farmingmentioning

confidence: 99%

Section: Information System Architecture For Smart Farmingmentioning

confidence: 99%

Farming in the Era of Internet of Things: An Information System Architecture for Smart Farming

Strobel¹

2020

WI2020 Community Tracks

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“…The analysis measures the simulated load time from different locations. Load time is an important aspect for measuring the performance of web applications [7]. This influences the user experience on the application.…”

Section: Performance Analysismentioning

confidence: 99%

“…The performance tests were done with the help of Web- PageTest¹, which is an online tool that allows complex load testing of web applications from different locations around the world. The test locations were chosen based on the existing literature [7]. The test results are presented on Table 1.…”

Section: Performance Analysismentioning

confidence: 99%

Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

Oteyo

Toili

2020

Open Computer Science

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Researchers in bio-sciences are increasingly harnessing technology to improve processes that were traditionally pegged on pen-and-paper and highly manual. The pen-and-paper approach is used mainly to record and capture data from experiment sites. This method is typically slow and prone to errors. Also, bio-science research activities are often undertaken in remote and distributed locations. Timeliness and quality of data collected are essential. The manual method is slow to collect quality data and relay it in a timely manner. Capturing data manually and relaying it in real time is a daunting task. The data collected has to be associated to respective specimens (objects or plants). In this paper, we seek to improve specimen labelling and data collection guided by the following questions; (1) How can data collection in bio-science research be improved? (2) How can specimen labelling be improved in bio-science research activities? We present WebLog, an application that we prototyped to aid researchers generate specimen labels and collect data from experiment sites. We use the application to convert the object (specimen) identifiers into quick response (QR) codes and use them to label the specimens. Once a specimen label is successfully scanned, the application automatically invokes the data entry form. The collected data is immediately sent to the server in electronic form for analysis.

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“…Developed a cost-effective mobile application for real-time monitoring of water and fertilizer in the field based on IOT. 81 Real-time agricultural decision-making platform. Proposed a real-time DSS for smart farming using cloud computing technology.…”

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confidence: 99%

State‐of‐the‐art technologies in precision agriculture: a systematic review

2019

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BACKGROUND The socio‐economic status of the countries is ever‐changing due to the increasing population. ‘Green Revolution’, a paradigm shift towards technology driven agriculture, has ensured a good quality of life to some extent for this increasing population. However, it brings in several negative impacts on the environment, which poses a threat to the sustainability of agriculture and natural resources. Precision agriculture (PA) is a state‐of‐the‐art concept of site‐specific farm management that helps to overcome this threat in a smart way using modern information and communication technologies. It reduces the indecorous use of resources, pollution and hence improves quality of life, which in turn helps to achieve sustainable development goals. RESULTS The objective of this systematic review is to understand the present status, benefits, and limitations of the state‐of‐the‐art technologies used in PA. A total 67 articles are identified following the PRISMA guideline to inspect the technical innovations at different components of PA. The articles are examined based on the novelties, measured parameters, technologies, and field of applications. CONCLUSION This analysis results in a significant understanding about the present knowledge gap and identification of the potential future research opportunities for sustainable agronomy. © 2019 Society of Chemical Industry

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CLUeFARM: Integrated web-service platform for smart farms

Cited by 47 publications

References 10 publications

Farming in the Era of Internet of Things: An Information System Architecture for Smart Farming

Farming in the Era of Internet of Things: An Information System Architecture for Smart Farming

Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

State‐of‐the‐art technologies in precision agriculture: a systematic review

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