Study on AI Technology Empowerment of Smart Farms from the Perspective of Science Communication: A Case Study of Primary and Secondary School Students and Science and Technology Counselor
DOI:
https://doi.org/10.61360/BoniCETR252019191205Keywords:
smart farm, AI technology, science communication, primary and secondary school students’ education, transformation of science and technology counselorAbstract
This study is conducted in accordance with China’s strategy for advancing agricultural and rural modernization during the 14th Five-Year Plan period and the requirements of the Central No. 1 Document in 2025. It explores the application of AI technology in smart farms and the value of conducting agricultural-related science communication among adolescents. Given the current situation where primary and secondary school students have certain misunderstandings and a lack of knowledge about modern agriculture, the study investigates the technical logic and social significance of AI technology in addressing educational pain points. It also focuses on the new models of science communication for primary and secondary school students and the transformation of the role of science and technology counselor. Based on the analysis of the actual situations of science popularization lectures and activities, the study proposes research methods to further improve the scientific literacy of primary and secondary school students and the professional capabilities of science and technology counselor.
References
Burns, T. W., O’Connor, D. J., & Stocklmayer, S. M. (2003). Science communication: A contemporary definition. Public Understanding of Science, 12(2), 183 - 202.
Chen, R. Y., Lin, X. W., & Zheng, H. R. (2021). Current application and future prospects of artificial intelligence in the agricultural field. Journal of Agricultural Modernization Research, 42(3), 456 - 464.
Huang, F. Y. (2024). Theory and implementation of primary school artificial intelligence project-based learning under interdisciplinary integration. Basic Education Research, 2024(12), 41-59. https://doi.org/10.19980/j.CN23-1593/G4.2025.08.030
Li, M. X., Zhang, X. F., & Wang, L. H. (2022). Research on crop disease recognition system based on deep learning. Transactions of the Chinese Society of Agricultural Engineering, 38(12), 156 - 164.
Liu, J. H., Sun, Y. X., & Zhou, M. H. (2022). Innovation in science communication models in the construction of smart farms. Research in Science Popularization, 17(2), 34 - 40.
Wang, Y., Chen, X., & Li, S. (2023). IoT-based smart irrigation system using deep learning. Computers and Electronics in Agriculture, 204, 107532.
Zhang, X. Y., Wei, M., & Liu, J. N. (2025). Discussion on the improvement of the teaching quality of agricultural practice under the background of new agricultural science construction. Higher Education Journal, 11(8), 126 - 129. https://doi.org/10.19980/j.CN23-1593/G4.2025.08.030
Zhang, Y. X., Zhao, S. H., & Liu, Y. (2020). Current situation and improvement strategies of agricultural science education in primary and secondary schools. China Electric Education, 2020(5), 45 - 51.
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Copyright (c) 2025 Yafang Liu
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