Role of Blockchain in Agriculture and Food Sector: A Summary

Kamalakshi, N.; Naganna,

doi:10.1007/978-3-030-76216-2_6

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Blockchain has been utilized in the global food and agriculture industry for several purposes, including farmer land registries, tracing the provenance of humanitarian goods, minimizing waste for small cooperative farmers, and guaranteeing that it reaches its intended users (Kamilaris et al, 2021) [35] . Blockchain has the potential to introduce the idea of agriculture insurance, in which pricing can adjust based on crop harvest status, and secure information can be sent down the supply chain, improving financial assistance and easing the burden on farmer operators (Kamalakshi & Naganna, 2021) [33] . Several possible benefit categories are offered by the application of blockchain to supply chain traceability: an expansion of the market based on quality assurance, Price increases, and value capture depending on consumer value is distributed via smart contracts to supply chain participants (Agrawal et al, 2023) [2] .…”

Section: Blockchainmentioning

confidence: 99%

Impact of disruptive technologies on transforming Indian agriculture

Pradhan,

Prusty,

Priyadarshi

et al. 2024

Int. J. Agric. Extension Social Dev.

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The concept of endogenous and exogenous agricultural disruptive technology is based on the idea and fundamentals of scientific and technological revolution. The process by which a major disruptive core technology innovation is applied to agricultural production propels several technological improvements adopted in production, and this is known as the "agricultural science and technology revolution." We are still in the experimental stage of developing artificial intelligence-based intelligent agriculture technology. Unlike the agricultural equipment, chemical, and green technological revolutions, agricultural scientists do not dominate smart agriculture technology, which is a transboundary application of agricultural technology with intelligent sensing technology at its foundation. Big data and machine learning are two examples of disruptive technologies (DTs) implemented more quickly in the farming industry thanks to the emergence of information and communication technologies (ICTs). Using these innovative and predictive techniques in agriculture is essential to managing hitherto unseen problems like climate change and population growth. Farming has been impacted by various disruptive and emerging technologies, including the Internet of Things (IoT), Artificial Intelligence, Blockchain, Unmanned Vehicles, Decision Support Systems (DSS), and Robotics.

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Section: Blockchainmentioning

confidence: 99%

Impact of disruptive technologies on transforming Indian agriculture

Pradhan,

Prusty,

Priyadarshi

et al. 2024

Int. J. Agric. Extension Social Dev.

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The integration of blockchain and IoT edge devices for smart agriculture: Challenges and use cases

Sugandh

Khari

Nigam

2022

Advances in Computers

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Research Constituents and Trends in Smart Farming: An Analytical Retrospection from the Lens of Text Mining

Sharma,

Asenso

et al. 2023

Journal of Sensors

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Agriculture research began with the idea that local systems are interconnected. Thus, it was crucial to consider farmers, crops, and livestock. Smart farming arose with the Internet of Things (IoT) as people progressively digitized farming with new information technology. Academic and scientific groups innovate and commercialize IoT-based agricultural products and solutions. Many public and private organizations also explore farming advancements. Therefore, we must stimulate communication and cooperation among the many farming industry actors to build smart agricultural standards and improve system and technology interoperability. The study analyzed 3,229 published articles from smart farming systems in the Scopus database between 2008 and 2022. The collected corpus is preprocessed through various steps, creating a bag of words. Text mining, latent semantic analysis, and network analysis are applied to the collected corpus to provide current research areas based on the key terms. The key terms are taken based on the term frequency-inverse document frequency score. The research was experimented with using KNIME and VOSviewer. Finally, 10 current research areas are provided using K-means clustering for a future researcher for deep insight. This study offered years of analysis, top journals, top authors, and leading countries contributing to smart farming. Research into the myriad issues facing smart farming could begin with these trends. This research helps future researchers understand smart farming and areas that need more attention. It also draws attention to research directions that could use further study.

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Role of Blockchain in Agriculture and Food Sector: A Summary

Cited by 6 publications

References 15 publications

Impact of disruptive technologies on transforming Indian agriculture

Impact of disruptive technologies on transforming Indian agriculture

The integration of blockchain and IoT edge devices for smart agriculture: Challenges and use cases

Research Constituents and Trends in Smart Farming: An Analytical Retrospection from the Lens of Text Mining

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