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Implementation of Artificial Intelligence Systems for Agri-food Supply Chains: A Bibliometric Approach

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Purpose: The present study is developed with the aim of mapping the trends in scientific production related to the implementation of artificial intelligence systems for agro-food supply chains. Research design, data and methodology: The methodological approach of the research shows a descriptive documentary process based on bibliometric techniques for mapping the main indicators of the area of knowledge through the establishment of a search equation in Scopus. Results: The research results show a total of 633 documents published between 2019 and 2023, with a great annual growth rate of 61.68%; In addition to a notable participation of countries such as India, China, the United Kingdom and the United States in the generation of new knowledge related to artificial intelligence applied to food distribution systems. Conclusions: It is concluded that the rise of new artificial intelligence technologies has shown extremely important results in the development of industries worldwide, with increasingly accelerated steps; which certainly translates into the creation of spaces and incentives in the production of research aimed at understanding these dynamics and in turn to propose new alternatives and proposals for the reduction of the large technological gaps that are present within the agro-food sector.

Abstract

Purpose: The present study is developed with the aim of mapping the trends in scientific production related to the implementation of artificial intelligence systems for agro-food supply chains. Research design, data and methodology: The methodological approach of the research shows a descriptive documentary process based on bibliometric techniques for mapping the main indicators of the area of knowledge through the establishment of a search equation in Scopus. Results: The research results show a total of 633 documents published between 2019 and 2023, with a great annual growth rate of 61.68%; In addition to a notable participation of countries such as India, China, the United Kingdom and the United States in the generation of new knowledge related to artificial intelligence applied to food distribution systems. Conclusions: It is concluded that the rise of new artificial intelligence technologies has shown extremely important results in the development of industries worldwide, with increasingly accelerated steps; which certainly translates into the creation of spaces and incentives in the production of research aimed at understanding these dynamics and in turn to propose new alternatives and proposals for the reduction of the large technological gaps that are present within the agro-food sector.

참고문헌 (74)

  1. Ahad, M. A., "Enabling technologies and sustainable smart cities", Sustainable cities and society, 61(1), pp.1~12 (2020)
  2. Alam, M., "Survey on deep neural networks in speech and vision systems", Neurocomputing, 417(1), pp.302~321 (2020)
  3. Allaoui, H., "Decision support for collaboration planning in sustainable supply chains", Journal of Cleaner Production, 229(1), pp.761~774 (2019)
  4. Amentae, T. K., "Digitalization and future agro-food supply chain management : A literature-based implications", Sustainability, 13(21), pp.1~24 (2021)
  5. Antonucci, F., "A review on blockchain applications in the agri-food sector", Journal of the Science of Food and Agriculture, 99(14), pp.6129~6138 (2019)
  6. Atukunda, P., "Unlocking the potential for achievement of the UN Sustainable Development Goal 2 –‘Zero Hunger’– in Africa : targets, strategies, synergies and challenges", Food & nutrition research, 65(1), pp.1~11 (2021)
  7. Blesh, J., "Development pathways toward"zero hunger"", World Development, 118(1), pp.1~14 (2019)
  8. Bottani, E., "Resilient food supply chain design : Modelling framework and metaheuristic solution approach", Computers & Industrial Engineering, 135(1), pp.177~198 (2019)
  9. Byerlee, D., "The SDG of zero hunger 75 years on : Turning full circle on agriculture and nutrition", Global Food Security, 21(1), pp.52~59 (2019)
  10. Chemura, A., "Impacts of climate change on agro-climatic suitability of major food crops in Ghana", PLoS One, 15(6), pp.1~21 (2020)
  11. Chen, L., "Artificial intelligence in education : A review", Ieee Access, 8(1), pp.75264~75278 (2020)
  12. Cubric, M., "Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study", Technology in Society, 62(1), pp.1~20 (2020)
  13. Dalmaijer, E. S., "Statistical power for cluster analysis", BMC bioinformatics, 23(1), pp.205~ (2022)
  14. Dhamija, P., "Role of artificial intelligence in operations environment : a review and bibliometric analysis", The TQM Journal, 32(4), pp.869~896 (2020)
  15. Di Vaio, A., "Artificial intelligence in the agri-food system : Rethinking sustainable business models in the COVID-19 scenario", Sustainability, 12(12), pp.1~12 (2020)
  16. Divyashree, D., "Artificial Intelligence and Deep Learning Based Agri & Food Quality and Safety Detection System", International Journal of Intelligent Systems and Applications in Engineering, 12(1s), pp.61~70 (2024)
  17. Dwivedi, Y. K., "Artificial Intelligence(AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy", International Journal of Information Management, 5(1), pp.1~97 (2021)
  18. Ekman, M, "Learning deep learning: Theory and practice of neural networks, computer vision, natural language processing, and transformers using TensorFlow" (2021), Addison-Wesley Professional
  19. El Jaouhari, A., "Assessing the influence of artificial intelligence on agri-food supply chain performance : the mediating effect of distribution network efficiency", Technological Forecasting and Social Change, 200(1), pp.123~149 (2024)
  20. El-Komy, A., "Integration of computer vision and natural language processing in multimedia robotics application", Inf. Sci, 7(6), pp.1~12 (2022)
  21. El-Ramady, H., "An overview of agro-waste management in light of the water-energy-waste nexus", Sustainability, 14(23), pp.1~30 (2022)
  22. Espejo-Garcia, B., "Towards weeds identification assistance through transfer learning", Computers and Electronics in Agriculture, 171(1), pp.1~12 (2020)
  23. Ezugwu, A. E., "Machine learning research trends in Africa : a 30 years overview with bibliometric analysis review", Archives of Computational Methods in Engineering, 30(7), pp.4177~4207 (2023)
  24. Galetsi, P., "Big data analytics in health : An overview and bibliometric study of research activity", Health Information & Libraries Journal, 37(1), pp.5~25 (2020)
  25. Ghosh, A., "Patterns and trends in Internet of Things (IoT) research: future applications in the construction industry", Engineering, Construction and Architectural Management, 28(2), pp.457~481 (2021)
  26. Guo, L., "Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks", Advanced Materials, 32(45), pp.1~8 (2020)
  27. Haiyun, C., "Analysis of the innovation strategies for green supply chain management in the energy industry using the QFD-based hybrid interval valued intuitionistic fuzzy decision approach", Renewable and Sustainable Energy Reviews, 143(1), pp.1~12 (2021)
  28. Hamrani, A., "Machine learning for predicting greenhouse gas emissions from agricultural soils", Science of The Total Environment, 741(1), pp.1~12 (2020)
  29. Henríquez-Calvo, L., "Analysis of the Impact Process Innovation and Collaboration on Competitiveness in Small and Medium-sized Enterprises : A Case Study in Colombia", Procedia Computer Science, 231(1), pp.636~641 (2024)
  30. Iwendi, C., "Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model", IEEE access, 8(1), pp.28462~28474 (2020)
  31. Jagtap, S., "Food logistics 4. 0 : Opportunities and challenges", Logistics, 5(1), pp.1~19 (2020)
  32. Jan, Z., "Artificial intelligence for industry 4. 0 : Systematic review of applications, challenges, and opportunities", Expert Systems with Applications, 216(1), pp.1~12 (2023)
  33. Javaid, M., "Enhancing smart farming through the applications of Agriculture 4. 0 technologies", International Journal of Intelligent Networks, 3(1), pp.150~164 (2022)
  34. Katal, A., "Innovative Trends in Computational Intelligence" (2022), Springer
  35. Khan, P. W., "IoT-blockchain enabled optimized provenance system for food industry 4. 0using advanced deep learning", Sensors, 20(10), pp.1~24 (2020)
  36. Khanal, U., "Farm performance analysis : Technical efficiencies and technology gaps of Nepalese farmers in different agroecological regions", Land use policy, 76(1), pp.645~653 (2018)
  37. Kong, J., "Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture", Computers and Electronics in Agriculture, 185(1), pp.106~134 (2021)
  38. Lezoche, M., "Agri-food 4. 0 : A survey of the supply chains and technologies for the future agriculture", Computers in industry, 117(1), pp.1~15 (2020)
  39. Liu, Y., "From industry 4. 0 to agriculture 4. 0 : Current status, enabling technologies, and research challenges", IEEE transactions on industrial informatics, 17(6), pp.4322~4334 (2020)
  40. Maulud, D. H., "Review on natural language processing based on different techniques", Asian Journal of Research in Computer Science, 10(1), pp.1~17 (2021)
  41. Meera, S., "Natural language processing" (2022), Scrivener Publishing LLC
  42. Misra, N. N., "IoT, big data, and artificial intelligence in agriculture and food industry", IEEE Internet of things Journal, 9(9), pp.6305~6324 (2020)
  43. Mochizuki, Y., "Rethinking Schooling for the 21st Century : UNESCO-MGIEP's Contribution to SDG 4. 7", Sustainability : The Journal of Record, 12(2), pp.88~92 (2019)
  44. Monteiro, J., "Artificial intelligence in extended agri-food supply chain : A short review based on bibliometric analysis", Procedia Computer Science, 192(1), pp.3020~3029 (2021)
  45. Moosavi, J., "Supply chain disruption during the COVID-19 pandemic : Recognizing potential disruption management strategies", International Journal of Disaster Risk Reduction, 75(1), pp.1~34 (2022)
  46. Naranjo, J. A. H., "The internet of things IoT : applications and perspectives in agriculture", Innovagro, 1(1), pp.16~20 (2022)
  47. Ness, S., "Synergy Between AI and Robotics : A Comprehensive Integration", Asian Journal of Research in Computer Science, 16(4), pp.80~94 (2023)
  48. Nhu, V. H., "Shallow landslide susceptibility mapping : A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms", International journal of environmental research and public health, 17(8), pp.1~30 (2020)
  49. Orbay, M., "Analysis of the journal impact factor and related bibliometric indicators in education and educational research category", Education for Information, 37(3), pp.315~336 (2021)
  50. Paciarotti, C., "The logistics of the short food supply chain : A literature review", Sustainable Production and Consumption, 26(1), pp.428~442 (2021)
  51. Pishdar, M., "Identifying and Prioritizing Factors Affecting Transportation Risk Management in the Food Supply Chain Using Gray Delphi And Gray COPRAS", Industrial Management Studies, 21(71), pp.263~295 (2023)
  52. Sahu, A., "Lotka’s law and author productivity pattern of research in law discipline", Collection and Curation, 41(2), pp.62~73 (2022)
  53. Sarker, I. H., "Machine learning : Algorithms, real-world applications and research directions", SN computer science, 2(3), pp.1~23 (2021)
  54. Sharifmousavi, M., "Distributed Artificial Intelligence Application in Agri-food Supply Chains 4. 0", Procedia Computer Science, 232(1), pp.211~220 (2024)
  55. Sharma, V. K., "Green supply chain management related performance indicators in agro industry : A review", Journal of cleaner production, 141(1), pp.1194~1208 (2017)
  56. Singh, B. N., "Effect of emerging contaminants on crops and mechanism of toxicity", Sustainable Agriculture Reviews, 40(1), pp.217~241 (2020)
  57. Singh, V. K., "Sustainable Agriculture Reviews 50:Emerging Contaminants in Agriculture" (2021), Springer
  58. Smith, M. J., "Getting value from artificial intelligence in agriculture", Animal Production Science, 60(1), pp.46~54 (2018)
  59. Soori, M., "Artificial intelligence, machine learning and deep learning in advanced robotics, a review", Cognitive Robotics, 3(1), pp.54~70 (2023)
  60. Sridhar, A., "Digitalization of the agro-food sector for achieving sustainable development goals : a review", Sustainable Food Technology, 1(3), pp.783~802 (2023)
  61. Sudhier, K. G., "Application of bradford’s law of scattering to the physics literature : a study of doctoral theses citations at the indian institute of science", DESIDOC Journal of Library &Information Technology, 30(2), pp.03~14 (2020)
  62. Taneja, A., "Artificial intelligence: Implications for the agri-food sector", Agronomy, 13(5), pp.1~20 (2023)
  63. Umar, M., "Do green finance and innovation matter for environmental protection? A case of OECD economies", Energy Economics, 119(1), pp.1~12 (2023)
  64. Walling, E., "Greenhouse gas emissions from inorganic and organic fertilizer production and use : A review of emission factors and their variability", Journal of Environmental Management, 276(1), pp.1~49 (2020)
  65. Wamba, S. F., "Blockchain in the operations and supply chain management : Benefits, challenges and future research opportunities", International Journal of Information Management, 52(1), pp.1~12 (2020)
  66. Wang, S., "Weakly supervised deep learning for segmentation of remote sensing imagery", Remote Sensing, 12(2), pp.1~25 (2020)
  67. Wongnaa, C. A., "Achieving sustainable development goals on no poverty and zero hunger : Does technical efficiency of Ghana’s maize farmers matter?", Agriculture & Food Security, 7(1), pp.1~13 (2018)
  68. Xu, Y., "Artificial intelligence : A powerful paradigm for scientific research", The Innovation, 2(4), pp.1~20 (2021)
  69. Yadav, V. S., "Exploring the application of Industry 4. 0technologies in the agricultural food supply chain : A systematic literature review", Computers & Industrial Engineering, 169(1), pp.1~12 (2022)
  70. Zhang, X., "An optimal decision for the fresh food supply chain network under omnichannel context", International Journal of Logistics Research and Applications, 25(1), pp.1~42 (2022)
  71. Zhong, Y., "The nexus among artificial intelligence, supply chain and energy sustainability : A time-varying analysis", Energy Economics, 132(1), pp.1~12 (2024)
  72. Zhou, L., "Application of deep learning in food : a review", Comprehensive reviews in food science and food safety, 18(6), pp.1793~1811 (2019)
  73. Zhou, S. K., "A review of deep learning in medical imaging : Imaging traits, technology trends, case studies with progress highlights, and future promises", Proceedings of the IEEE, 109(5), pp.820~838 (2021)
  74. Zou, T., "Global trends of cropland phosphorus use and sustainability challenges", Nature, 611(7934), pp.81~87 (2022)

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