| Issue |
BIO Web Conf.
Volume 189, 2025
11th International Conference on Sustainable Agriculture, Food, and Energy (SAFE 2025)
|
|
|---|---|---|
| Article Number | 03002 | |
| Number of page(s) | 17 | |
| Section | Sustainability Development, Management, and Policy | |
| DOI | https://doi.org/10.1051/bioconf/202518903002 | |
| Published online | 09 October 2025 | |
Evaluating Knowledge Creation in Smart Container for Monitoring and Prediction of Fresh Fruit Quality: A Systematic Literature Review
1 Department of Agriculture and Business, Politeknik Negeri Ketapang, Ketapang 78813 Indonesia
2 Department of Agroindustrial Technology, Faculty of Agricultural Technology and Engineering, Bogor Agricultural University (IPB University), Bogor 16880 Indonesia
3 National Research and Innovation Agency, BRIN, Indonesia
* Corresponding author: encikrifkowaty@apps.ipb.ac.id
Post-harvest fruit losses continue to exceed 30% in many emerging economic. While smart containers equipped with Internet-of-Things (IoT) sensors and artificial-intelligence (AI) models can mitigate spoilage, most systems treat data as an end in itself rather than as a raw material for knowledge creation (KC). Applying PRISMA-2020 and bibliometric mapping (VOSviewer & Bibliometrix), we screened 343 Scope records (2003-2025) and analysed 43 primary studies. The key finding is that more than 70% of the literature operationalises only the Combination phase of the SECI model (data aggregation and predictive modelling). Socialization (tacit knowledge sharing) and Internalization (experiential learning) are almost absent, thereby limiting adaptive decision-making and stakeholder engagement. We conclude with a research-practice roadmap that embeds the complete SECI cycle into smart- container design.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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