Research on Agricultural Supply Chain Architecture Based on Edge Computing and Efficiency Optimization

Cui, Haiyun

doi:10.1109/access.2021.3113723

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…This development is crucial for the agricultural sector, where efficient inventory management and waste reduction are key to sustainability and profitability. Cui (2022) proposes a classification standard of supply chain structure in agriculture based on edge computing and efficiency optimization. The study considers the characteristics of agriculture itself and the feasibility of solution strategies from different sides.…”

Section: Evolution and Future Trends In Ai-based Predictive Analytics...mentioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.

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Section: Evolution and Future Trends In Ai-based Predictive Analytics...mentioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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“…Each of these approaches can realise informatisation, digitisation, and progress in intelligent development. To address various concerns, such as the complexity of supply chain node enterprises, reliability of the supply chain, and optimisation objectives, some scholars have innovatively constructed a system model and proposed an optimisation strategy and supply solutions based on the agricultural characteristics and requirements of the agricultural supply chain [4]. Technologies, such as blockchain, have increased the transparency in various supply chains, and forecasting has increased their accountability.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Ye¹,

Jin²,

Jiang³

et al. 2023

Computers, Materials &Amp; Continua

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Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain. In an Internet of Things (IoT) environment, accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain. As an example, this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit (the Shatian pomelo) in a comparative study. The root means square error (RMSE) values of regression analysis, exponential smoothing, grey prediction, grey neural network, support vector regression (SVR), and long short-term memory (LSTM) neural network methods were 53.715, 6.707, 18.440, 1.580, and 1.436, respectively. Among these, the mean square error (MSE) values of the grey neural network, SVR, and LSTM neural network methods were 2.4979, 31.652, and 2.0618, respectively; and their R values were 0.99905, 0.94, and 0.94501, respectively. The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model, and the prediction results are more accurate. The prediction performance of the grey neural network was shown to be superior to that of SVR, and LSTM neural network, based on the comparison of parameters.

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Revolutionizing supply chain and circular economy with edge computing: systematic review, research themes and future directions

Akbari

2023

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PurposeThe purpose of this study is to examine how the implementation of edge computing can enhance the progress of the circular economy within supply chains and to address the challenges and best practices associated with this emerging technology.Design/methodology/approachThis study utilized a streamlined evaluation technique that employed Latent Dirichlet Allocation modeling for thorough content analysis. Extensive searches were conducted among prominent publishers, including IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi, utilizing pertinent keywords associated with edge computing, circular economy, sustainability and supply chain. The search process yielded a total of 103 articles, with the keywords being searched specifically within the titles or abstracts of these articles.FindingsThere has been a notable rise in the volume of scholarly articles dedicated to edge computing in the circular economy and supply chain management. After conducting a thorough examination of the published papers, three main research themes were identified, focused on technology, optimization and circular economy and sustainability. Edge computing adoption in supply chains results in a more responsive, efficient and agile supply chain, leading to enhanced decision-making capabilities and improved customer satisfaction. However, the adoption also poses challenges, such as data integration, security concerns, device management, connectivity and cost.Originality/valueThis paper offers valuable insights into the research trends of edge computing in the circular economy and supply chains, highlighting its significant role in optimizing supply chain operations and advancing the circular economy by processing and analyzing real time data generated by the internet of Things, sensors and other state-of-the-art tools and devices.

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Research on Agricultural Supply Chain Architecture Based on Edge Computing and Efficiency Optimization

Cited by 5 publications

References 22 publications

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Revolutionizing supply chain and circular economy with edge computing: systematic review, research themes and future directions

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