IoT and data interoperability in agriculture: A case study on the gaiasense<sup>TM</sup> smart farming solution

Kalatzis, Nikos; Marianos, Nikolaos; Chatzipapadopoulos, Fotis G.

doi:10.1109/giots.2019.8766423

Cited by 13 publications

(14 citation statements)

References 7 publications

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“…Although PestGaS is designed as an autonomous and interoperable sensing system, it is initially planned to be integrated and piloted with the “gaiasense” Smart Farming solution which already incorporates a wide range of sensing and data analytics technologies in order to offer next generation advice to farmers ( Kalatzis et al, 2019 ). A cornerstone for the “gaiasense” SF solution is the infrastructure of telemetric autonomous stations which collect data from sensors installed in the field; the sensors are used so as to record atmospheric (air temperature, relative moisture, wind direction and velocity, rain,) soil (temperature, salinity, moisture) and plant (leaf wetness, leaf humidity, leaf temperature) parameters.…”

Section: Introductionmentioning

confidence: 99%

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

Skotadis

Kanaris

Aslanidis

et al. 2020

Computers and Electronics in Agriculture

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Highlights A hybrid nanomaterial based gas-sensing array has been used for pesticide detection. The pesticide is the commercially available organophosphate based Chloract 48 EC. The array has successfully distinguished between relative humidity and pesticide. The successful operation of the array has been validated via the PCA method. This study expands the limited available results related to pesticide gas-sensors.

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Section: Introductionmentioning

confidence: 99%

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

Skotadis

Kanaris

Aslanidis

et al. 2020

Computers and Electronics in Agriculture

Self Cite

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“…There are noticeable improvements in latency, sensing, and actuating but, chances of tampering and manipulating data are highly probable. Specifically, the gaiasense™ smart farming framework is composed of IoT devices (or GAIAtrons), cloud computing services (or GAIA Cloud), and smart farming (SF) advisory services (or GAIA SmartFarm) [37]. However, privacy of data was not considered.…”

Section: Existing Framework Of Smart Farming Systemsmentioning

confidence: 99%

Smart Rice Precision Farming Schemes in Sub-Saharan Africa: Process and Architecture

Alfa¹,

Alhassan²,

Olaniyi³

et al. 2023

Sustainable Rice Production - Challenges, Strategies and Opportunities

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Smart farming integrates information, communication, and control technologies in agricultural practices. Recently, crop enterprise management through smart precision farming technologies are antidotes to uncontrollable soil and environmental factors compounded by climate change. Farm production planning utilizes enormous data generated from the field by human agents and IoT devices, but is often unreliable and inaccurate. These cause low yield, high losses, inferior quality of farm produce, overuse or underuse of fertilizers, increased costs, and inefficient farm management. Traditionally, analyzing rice cropping yields is time-inefficient and tasking, which led to quicker IoT adoption. Aside insufficient data sharing infrastructure, data privacy problem is widespread The blockchain technology is useful for verifying the reliability, accuracy, and authenticity of IoT data generated from fields for the production planning. In the future, dynamic systems (smart rice farming) and model-based control systems can be applied to understand the physical process and valuable factors of production. This paper provides a comprehensive state-of-the-art process and architectural survey on impacts of uncontrollable environmental factors, smart precision framework, security and privacy architectures or solutions for improving rice crop production. Again, a new taxonomy is developed to guide researchers, advance the course of rice production, and improve yields across sub-Saharan Africa.

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“…The applications of IoT composition cover multiple domains, including cities, buildings, transportation, health, farming, and manufacturing [121][23] [41]. Different stakeholders have diverse expectations and requirements regarding building or leveraging composite capabilities.…”

Section: ) Domains and Stakeholdersmentioning

confidence: 99%

“…Smart Farming [121] • Gaiasense offers novel, inexpensive composite smart farming services by facilitating data interoperability for smart-farming systems using techniques such as "Data Interoperability Zone" and "Information Management Adapter. "…”

Section: ) Composition Process Type and Automation Levelmentioning

confidence: 99%

IoT Capabilities Composition and Decomposition: A Systematic Review

et al. 2023

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As billions of IoT devices join the Internet, researchers and innovators increasingly explore IoT capabilities achieved via service composition or reuse of existing capabilities via service decomposition. Many systematic literature reviews (SLRs) were produced on this subject; however, two issues remain to be addressed: i) a reference taxonomy of the different aspects of IoT capabilities composition and decomposition is needed, and ii) many formal questions (e.g., standards role, formal representations applications, state-space explosion countermeasures, etc.), technical questions (e.g., composition process types and automation levels synergies, service decomposition categories, the role of AI/ML, etc.), and QoS questions (e.g., privacy, interoperability, and scalability challenges and solutions, etc.) remain unanswered. We introduce this work by discussing notions of IoT capabilities composition and decomposition in a layered IoT architecture while highlighting the strengths and weaknesses of existing SLRs. We identify unanswered questions through gaps in related work and motivate these questions using the PICOC methodology. We explain the search methodology and organize the topic questions using the proposed reference taxonomy. The identified research questions are answered, and trends and gaps that need additional attention from the research community are highlighted. This effort benefits city planners and end-users of IoT systems as it contributes to a better understanding of the role of composition and decomposition of IoT capabilities in building valueadded services or reusing existing ones for resource optimization. For researchers, this effort contributes a reference taxonomy for the topic and sheds light on important questions while highlighting corresponding trends and gaps requiring further attention.

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IoT and data interoperability in agriculture: A case study on the gaiasense^TM smart farming solution

Cited by 13 publications

References 7 publications

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

Smart Rice Precision Farming Schemes in Sub-Saharan Africa: Process and Architecture

IoT Capabilities Composition and Decomposition: A Systematic Review

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IoT and data interoperability in agriculture: A case study on the gaiasenseTM smart farming solution

Cited by 13 publications

References 7 publications

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

A sensing approach for automated and real-time pesticide detection in the scope of smart-farming

Smart Rice Precision Farming Schemes in Sub-Saharan Africa: Process and Architecture

IoT Capabilities Composition and Decomposition: A Systematic Review

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Product

Resources

About

IoT and data interoperability in agriculture: A case study on the gaiasense^TM smart farming solution