AI-Driven Approaches to Energy Management in Agricultural Cold Chains
Journal of Smart and Sustainable Farming
Received On : 02 March 2025
Revised On : 30 April 2025
Accepted On : 12 May 2025
Published On : 25 May 2025
Volume 01, 2025
Pages : 078-088
Abstract
Organizations operating within the cold chain logistics sector are adopting a strategic approach towards using big data in order to effectively navigate the substantial potential and challenges presented by Artificial Intelligence (AI) and Internet of Things (IoT). When used in the broader context of cold chain logistics, IoT technology enhances the traditional information transmission network. In recent years, there has been a notable emergence of research focused on technological breakthroughs in the field of Internet of Things. As technology progresses to a certain threshold, the model of application will undergo maturation, leading to an expansion in range of requisitions and an augmentation in their capabilities. This paper examines the prevailing concerns pertaining to energy consumption across the agricultural supply chain and asserts the indispensability of using AI as a means to address these challenges. Furthermore, this paper examines several aspects of energy system planning and operation, specifically focusing on the potential use of AI in addressing energy challenges within the cold supply chain. In conclusion, this study undertakes more research on the problems and potential of the next generation of super cold chain. Its aim is to establish a standard for the good expansion of cold-chain logistics within the agricultural industry, with major interest in minimizing carbon emissions.
Keywords
Super Cold Chains, Cold Chain Logistics, Artificial Intelligence, Internet of Things, Cold Chain Infrastructure, Cold Chain Industry.
- Z. Kegenbekov and A. Saparova, “Using the MQTT protocol to transmit vehicle telemetry data,” Transp. Res. Procedia, vol. 61, pp. 410–417, 2022.
- S. L. Myers, “China’s pledge to be carbon neutral by 2060: What it means,” The New York times, The New York Times, 23-Sep-2020.
- P. Chaitangjit and P. Ongkunaruk, “The study of cold storage and temperature-controlled transportation: A case study of a chain restaurant in Thailand,” Pamukkale Univ. J. Eng. Sci., vol. 25, no. 9, pp. 1014–1019, 2019.
- S. Fujita, “Similarity search in InterPlanetary file system with the aid of locality sensitive hash,” IEICE Trans. Inf. Syst., vol. E104.D, no. 10, pp. 1616–1623, 2021.
- V. S. Yadav, A. R. Singh, A. Gunasekaran, R. D. Raut, and B. E. Narkhede, “A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives,” Sustain. Prod. Consum., vol. 29, pp. 685–704, 2022.
- R. V. George, H. O. Harsh, P. Ray, and A. K. Babu, “Food quality traceability prototype for restaurants using blockchain and food quality data index,” J. Clean. Prod., vol. 240, no. 118021, p. 118021, 2019.
- W. Liang, J. Cao, Y. Fan, K. Zhu, and Q. Dai, “Modeling and implementation of cattle/beef supply chain traceability using a distributed RFID-based framework in China,” PLoS One, vol. 10, no. 10, p. e0139558, 2015.
- P. Kumar and R. Kumar Singh, “Strategic framework for developing resilience in Agri-Food Supply Chains during COVID 19 pandemic,” Int. J. Logist., vol. 25, no. 11, pp. 1401–1424, 2022.
- H. M. Hafez and Y. A. Attia, “Challenges to the poultry industry: Current perspectives and strategic future after the COVID-19 outbreak,” Front. Vet. Sci., vol. 7, 2020.
- D. Bumblauskas, A. Mann, B. Dugan, and J. Rittmer, “A blockchain use case in food distribution: Do you know where your food has been?,” Int. J. Inf. Manage., vol. 52, no. 102008, p. 102008, 2020.
- L. Hang, I. Ullah, and D.-H. Kim, “A secure fish farm platform based on blockchain for agriculture data integrity,” Comput. Electron. Agric., vol. 170, no. 105251, p. 105251, 2020.
- W. Ren, X. Wan, and P. Gan, “A double-blockchain solution for agricultural sampled data security in Internet of Things network,” Future Gener. Comput. Syst., vol. 117, pp. 453–461, 2021.
- K. Dey and U. Shekhawat, “Blockchain for sustainable e-agriculture: Literature review, architecture for data management, and implications,” J. Clean. Prod., vol. 316, no. 128254, p. 128254, 2021.
- M. Nasiri, J. Ukko, M. Saunila, and T. Rantala, “Managing the digital supply chain: The role of smart technologies,” Technovation, vol. 96–97, no. 102121, p. 102121, 2020.
- D. Fan and S. Gao, “The application research of modern intelligent cold chain distribution system based on internet of things technology,” in Applied Mechanics, Mechatronics and Intelligent Systems, 2016.
- N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, 2021.
- M. Shafique, A. Rashid, I. Bajwa, R. Kazmi, M. Khurshid, and W. Tahir, “Effect of IoT capabilities and energy consumption behavior on green supply chain integration,” Appl. Sci. (Basel), vol. 8, no. 12, p. 2481, 2018.
CRediT Author Statement
The author reviewed the results and approved the final version of the manuscript.
Conceptualization: Ting Li and Wan Fang; Methodology: Ting Li and Wan Fang; Software: Ting Li; Data Curation: Wan Fang; Writing-Original Draft Preparation: Ting Li; Validation: Ting Li and Wan Fang; Writing- Reviewing and Editing: Ting Li and Wan Fang; All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Wan Fang for this research completion and support.
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© 2025 Ting Li and Wan Fang. The author(s) retain copyright of the work. The author(s) grant the Journal of Smart and Sustainable Farming (JSSF) and its publisher, Ansis Publications, the right of first publication and the right to identify itself as the original publisher of the article.
Cite this Article
Ting Li and Wan Fang, “AI-Driven Approaches to Energy Management in Agricultural Cold Chains”, Journal of Smart and Sustainable Farming, pp. 078-088, 2025, doi: 10.64026/JSSF/2025008.