This study focused on smart farms in Korea to confirm that the fourth industrial revolution technology is a tool that can be used for sustainable economic development in agriculture. This study used the input–output table, which included the production-inducing effects of the demand inducement model, value-added inducing effects, supply shortage effects of the supply inducement model, and the interlinkage effect. These methods were used to compare the impact of smart farm machinery on agriculture and the impact of smart farms on the Korean economy compared to conventional agriculture, thereby confirming the extent of the effect of fourth industrial revolution technology on agriculture. This study was conducted to determine whether this agricultural sector could lead to sustainable economic development. The analysis revealed that the production-inducing effects of smart farms showed a greater ripple effect than those of the agricultural sector. However, both value-added inducing effects and supply shortage effects showed a larger ripple effect in the agricultural sector. Considering only the indirect effect, the smart farms were found to evenly affect secondary and tertiary industries. In the case of value-added inducing and supply shortage effects, agriculture was found to have a greater impact on the secondary industry, whereas smart agri-machines and farms had a greater impact on the tertiary industry. Moreover, according to the interlinkage effect, agriculture was classified as a raw industry with final demand, and smart farms as a manufacturing industry with intermediate demand. These results have several implications. For the fourth industrial revolution technology to lead sustainable economic growth in agriculture, continuous creation of new value by developing various types of business models linked to other industries in consideration of the industrial characteristics of smart farms and agri-machines is necessary. In addition, to fully demonstrate the economic effects of smart farms, continuous management and support are required so that smart technologies can be strategically utilized in the agricultural sector.

This study proposes a functional framework to enhance the efficiency and effectiveness of e-learning content creation by systematically integrating generative artificial intelligence (AI) technologies. While previous research on e-learning has primarily focused on systems and infrastructure, little attention has been given to content creation. To address this gap, we present a five-step methodology: (1) conducting a systematic literature review of existing e-learning development frameworks; (2) proposing a content-specific framework centered on instructors and technical support roles; (3) outlining a detailed task-based content creation process; (4) identifying and classifying commercial AI tools applicable to each functional unit; and (5) comparing the tools based on their strengths, limitations, and suitability. The proposed framework includes eight key functional stages, ranging from lesson planning to editing, automation, and final review. For each stage, AI tools such as ChatGPT, Synthesia, MidJourney, and Grammarly are evaluated and mapped to the corresponding workflow phase. The findings suggest that integrating AI tools into content creation can significantly reduce production time and cost, improve instructional quality, and lower e-learning sector entry barriers. This study contributes a conceptual model and practical strategies for leveraging AI in scalable, high-quality digital education environments.

Low-cost RGB intraoral cameras are accessible alternatives to intraoral scanners; however, generating panoramic images is challenging due to narrow fields of view, textureless surfaces, and specular highlights. This study proposes a robust stitching framework and identifies optimal acquisition parameters to overcome these limitations. All experiments were conducted exclusively on a mandibular dental phantom model. Geometric consistency was further validated using repeated physical measurements of mandibular arch dimensions as ground-truth references. We employed a deep learning-based approach using SuperPoint and SuperGlue to extract and match features in texture-poor environments, enhanced by a central-reference stitching strategy to minimize cumulative drift errors. To validate the feasibility in a controlled setting, we conducted experiments on dental phantoms varying working distances (1.5–3.0 cm) and overlap ratios. The proposed method detected approximately 19–20 times more valid inliers than SIFT, significantly improving matching stability. Experimental results indicated that a working distance of 2.5 cm offers the optimal balance between stitching success rate and image detail for handheld operation, while a 1/3 overlap ratio yielded superior geometric integrity. This system demonstrates that robust 2D dental mapping is achievable with consumer-grade sensors when combined with advanced deep feature matching and optimized acquisition protocols.