Recently, unmanned aerial vehicles (UAV, Drone) are highly regarded for their potential in the agricultural field, and research and development are actively conducted for various purposes. Therefore, in this study, to present a framework for tracking research trends in UAV use in the agricultural field, we secured a keyword search strategy and analyzed social network, a methodology used to analyze recent research trends or technological trends as an analysis model applied. This study consists of three stages. As a first step in data acquisition, search terms and search formulas were developed for experts in accordance with the Keyword Search Strategy. Data collection was conducted based on completed search terms and search expressions. As a second step, frequency analysis was conducted by country, academic field, and journal based on the number of thesis presentations. Finally, social network analysis was performed. The analysis used the open source programming language 'Python'. Thanks to the efficiency and convenience of unmanned aerial vehicles, this field is growing rapidly and China and the United States are leading global research. Korea ranked 18th, and bold investment in this field is needed to advance agriculture. The results of this study's analysis could be used as important information in government policy making.

In the present day context of changing information needs of the farmers and diversified production systems there is an urgent need to look for the effective extension support system for the small and marginal farmers in the developing countries like India. The rapid developments in the collection and analysis of field data by using the spatial technologies like GPS&GIS were made available for the extension functionaries and clientele for the diversified information needs. This article describes the GIS and GPS based decision support system in precision agriculture for the resource poor farmers. Precision farming techniques are employed to increase yield, reduce production costs, and minimize negative impacts to the environment. The parameters those can affect the crop yields, anomalous factors and variations in management practices can be evaluated through this GPS and GIS based applications. The spatial visualisation capabilities of GIS technology interfaced with a relational database provide an effective method for analysing and displaying the impacts of Extension education and outreach projects for small and marginal farmers in precision agriculture. This approach mainly benefits from the emergence and convergence of several technologies, including the Global Positioning System (GPS), geographic information system (GIS), miniaturised computer components, automatic control, in-field and remote sensing, mobile computing, advanced information processing, and telecommunications. The PPP convergence of person (farmer), project (the operational field) and pixel (the digital images related to the field and the crop grown in the field) will better be addressed by this decision support model. So the convergence and emergence of such information will further pave the way for categorisation and grouping of the production systems for the better extension delivery. In a big country like India where the farmers and holdings are many in number and diversified categorically such grouping is inevitable and also economical. With this premise an attempt has been made to develop a precision farming model suitable for the developing countries like India.

본 연구에서는 정밀농업 구현을 위해 필수적인 요소인 토양과 기후 그리고 위치요인을 데이터로 구축한 후 이를 수치화하여 최고점수제를 이용하는 지식기반형 작물추천모델을 개발하였다. 작물추천시스템에서 사용한 요인은 토양과 기후 그리고 위치정보이다. 토양의 경우에는 흙토람 자료 중 작물적지 선정에 80%이상 활용되는 토성, 침식등급, 배수등급, 경사, 자갈함량, pH를 선정하였다. 기후요인은 기상청의 5년간 누적데이터를 기초로 하여 작물생육에 필수적인 기온과 강수량 그리고일조시간 등을 사용하였다. 위치정보는 한국토지정보시스템과 수치지형도를 가공하여 사용하였다. 예시작물은 농촌진흥청에서 추천한 옥수수, 유채, 갈대, 수박, 고추, 토마토, 양파, 감자, 고구마, 구기자 10개 작물로 하였으며, 향후 작물추천 수요가증가할 것으로 예상되는 제천, 무안, 원주, 함안 4개 지역을 포괄적으로 시뮬레이션하였다. 분석결과 평균점수는 고추가 75.5점으로 가장 높았으며 고구마가 61.2점으로 가장 낮았다. 작물별 식재면적에 제약이 있다고 가정하고 2차 시뮬레이션을실시하여 경남 함안시의 7만 필지를 별도설정하고 분석한 결과 고구마가 39,190필지에서 가장 높은 점수를 받았으며 면적으로는 27.014 km2이었다. 지식기반형 작물추천시스템은 작물의 적지를 선정하는 항목과 가중치가 검증되지 않았다는 한계를 가지고 있으나, 이에 대한 보완이 이루어지고, 농산물유통부분을 고려하여 실제 정보수요자에게 제공하면 영농현장에서 활용 가능할 것으로 판단한다.