[논문]Analysis of the Present Status and Future Prospects for Smart Agriculture Technologies in South Korea Using National R&D Project Data

Lee, Sujin; Park, Jun-Hwan; Kim, EunSun; Jang, Wooseok

doi:10.1633/jistap.2022.10.s.11

[국내논문] Analysis of the Present Status and Future Prospects for Smart Agriculture Technologies in South Korea Using National R&D Project Data 원문보기

Journal of information science theory and practice : JISTaP, v.10 no.spc, 2022년, pp.112 - 122

Lee, Sujin (Division of Data Analysis, Korea Institute of Science and Technology Information (KISTI)) , Park, Jun-Hwan (Division of Data Analysis, Korea Institute of Science and Technology Information (KISTI)) , Kim, EunSun (Division of Data Analysis, Korea Institute of Science and Technology Information (KISTI)) , Jang, Wooseok (Division of Data Analysis, Korea Institute of Science and Technology Information (KISTI))

Abstract ▼ AI-Helper

Food security and its sovereignty have become among the most important key issues due to changes in the international situation. Regarding these issues, many countries now give attention to smart agriculture, which would increase production efficiency through a data-based system. The Korean government also has attempted to promote smart agriculture by 1) implementing the agri-food ICT (information and communications technology) policy, and 2) increasing the R&D budget by more than double in recent years. However, its endeavors only centered on large-scale farms which a number of domestic farmers rarely utilized in their farming. To promote smart agriculture more effectively, we diagnosed the government R&D trends of smart agriculture based on NTIS (National Science and Technology Information Service) data. We identified the research trends for each R&D period by analyzing three pieces of information: the regional information, research actor, and topic. Based on these findings, we could suggest systematic R&D directions and implications.

주제어

표/그림 (5)

그림 Fig. 1. Descriptive statistics of National R&D projects for smart agriculture.
표 Table 1. Result of regional R&D investment analysis (representing only top 10 provinces)
표 Table 2. Result of the research actor analysis
표 Table 3. Major topics of each phase
표 Table 4. Result of keyword analysis

AI 본문요약
AI-Helper

제안 방법

The study in Phase I was conducted mainly for the preparation of the basis of data construction and for improvement in convenience through remote monitoring and control. In Phase II, the primary research was performed by applying ICT convergence technology for smart farming and unmanned automation using harvest robots, drones, and intelligent agricultural ma- chines, and for supporting decision-making (prediction, confrontation, management, and so on) to produce crops optimally based on the data related to cultivation environment and growth. Also, it was found in Phase III that the research was focused on the spread of smart agriculture including (1) the optimal management of complex energy using new and renewable energy (energy saving), (2) the establishment of the cloud-based big data platform for arrangement in the foundation of data connection, sharing, and utilization, (3) the standardization of environment and growth data and apparatuses for enhancement in the compatibility between devices, and (4) the training of professional manpower.
In addition, to diagnose the previous technology trends for smart agriculture, we analyzed the R&D trend by classifying national R&D projects into three phases in consideration of the government’s mid-to-long- term policy
From the viewpoint of R&D characteristics by region, the R&D investment amount of Jeollabuk-do, which is the selected area for the Smart Farm Innovation Valley project, increased significantly in Phase III with government policy and R&D budget shifting gradually from the metropolitan area to the provinces. The study in Phase I was conducted mainly for the preparation of the basis of data construction and for improvement in convenience through remote monitoring and control. In Phase II, the primary research was performed by applying ICT convergence technology for smart farming and unmanned automation using harvest robots, drones, and intelligent agricultural ma- chines, and for supporting decision-making (prediction, confrontation, management, and so on) to produce crops optimally based on the data related to cultivation environment and growth.

대상 데이터

The data collected by NTIS was limited to smart agriculture in the production stage, and a total of 4, 935 national R&D projects were derived from 2012 to 2020 through a search formula that reflected the opinions of experts

성능/효과

1, both the number and budget of national R&D projects have steadily increased
In Phase II, the primary research was performed by applying ICT convergence technology for smart farming and unmanned automation using harvest robots, drones, and intelligent agricultural ma- chines, and for supporting decision-making (prediction, confrontation, management, and so on) to produce crops optimally based on the data related to cultivation environment and growth. Also, it was found in Phase III that the research was focused on the spread of smart agriculture including (1) the optimal management of complex energy using new and renewable energy (energy saving), (2) the establishment of the cloud-based big data platform for arrangement in the foundation of data connection, sharing, and utilization, (3) the standardization of environment and growth data and apparatuses for enhancement in the compatibility between devices, and (4) the training of professional manpower.
First of all, the Korean government should prepare comprehensive strategies for the diffusion of smart agriculture in order to 1) reflect the properties of domestic agriculture, and 2) extend the R&D fields
However, our research has some limitations. First, our gathered data itself has limitations. We attempted to investigate the recent R&D trends for smart agriculture, but the information on national R&D research projects from 2021 to 2022 has not been aggregated yet.
From the viewpoint of R&D characteristics by region, the R&D investment amount of Jeollabuk-do, which is the selected area for the Smart Farm Innovation Valley project, increased significantly in Phase III with government policy and R&D budget shifting gradually from the metropolitan area to the provinces
In addition, the R&D budget in the private sector was higher than that of the public sector
Second, we rarely considered the outcome of R&D projects in the suggested framework
38% from 2012 to 2020, with rapid increment of its value after 2018. The research on big data platforms based on cloud systems for diffusing smart agriculture sharply grew in Phase III. From the viewpoint of R&D characteristics by region, the R&D investment amount of Jeollabuk-do, which is the selected area for the Smart Farm Innovation Valley project, increased significantly in Phase III with government policy and R&D budget shifting gradually from the metropolitan area to the provinces.
The scale of R&D investment by step increased at an average annual rate of 14.38% from 2012 to 2020, with rapid increment of its value after 2018
Therefore, our frameworks could systematically identify the R&D trends for smart agriculture, and these comprehensive approaches could represent the insufficient R&D parts for the Korean government and related stakeholders
These policies have a strong influence on R&D trends, and our research could capture these changing trends
Ultimately, for the realization of sustainable agricul- ture, the government should construct a cooperative ecosystem between stakeholders. This ecosystem could improve productivity, safety, economic feasi- bility, and quality of the entire production-distribu- tion-sale-consumption cycle.
To conduct these strategies, the government should promote four continuous R&D directions: (1) the expansion of application areas, (2) the reinforcement of software, (3) the inclusion of the forward and backward linkage throughout the agricultural value chain, and (4) the training of human resources.

후속연구

. In addition, it is also necessary to expand research fields on data standardization closely related to service applications. Since 2021, governments and institutes have collected agricultural data as a result of the national R&D projects.
We intend to plan further R&D performance analysis by analyzing the academic performance (paper, patent) and technical performance (technology alliance and transfers) from NTIS data. In this process, various analyzable indicators such as diversity index and the attractive index will also be introduced in future research.
Our future studies will validate the usability of the suggested framework by analyzing the extended time range and a wider variety of R&D projects from the private sector
They shared these data, such as on soil, weather, pests, and environment, for free use. These data could promote the understanding and cooperation of several research actors if a standardized process for the agricultural data and equipment could be constructed.
. Ultimately, for the realization of sustainable agricul- ture, the government should construct a cooperative ecosystem between stakeholders. This ecosystem could improve productivity, safety, economic feasi- bility, and quality of the entire production-distribu- tion-sale-consumption cycle.
We intend to plan further R&D performance analysis by analyzing the academic performance (paper, patent) and technical performance (technology alliance and transfers) from NTIS data

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