Abstract
The abstract discusses the increasing importance of drones, also known as Unmanned Aerial Vehicles (UAVs), in agriculture due to their ability to enhance farming practices by reducing costs, improving efficiency, and increasing profitability. The study uses bibliometric analysis to review existing academic literature on agricultural drones, highlighting key research areas such as remote sensing, precision agriculture, deep learning, machine learning, and the Internet of Things, and identifies future research directions in this field.
Analysis starts
The introduction discusses the challenges faced by agriculture due to increasing food demand, environmental concerns, and sustainability issues. It highlights the importance of innovative technologies like smart farming and precision agriculture to address these challenges. The text emphasizes the role of technologies such as drones, IoT, AI, and remote sensing in revolutionizing farming practices for increased efficiency and sustainability.
Case analysis
Citation analysis is a method used to examine the influence and impact of publications within a specific research field. It helps identify the most influential authors and publications, understand knowledge flow and communication links between researchers, and track the evolution of a research domain over time by analyzing how publications cite each other. This analysis provides insights into the relevance, popularity, and progress of research works in a particular field based on their citation patterns.
Data collection and analysis
In the context of the research study on drone applications in agriculture, the data collection and analysis involved comprehensive steps such as retrieving relevant publications from databases like Scopus, analyzing key terms, exploring citation patterns to identify influential authors and publications, conducting co-citation analysis to group similar publications, and examining collaboration networks between countries, institutions, and journals. These methods helped in understanding the intellectual structure, research trends, and connections within the field of agricultural drones, providing insights into the current state and future directions of research in this domain.
Keyword analysis
Keywords analysis is a method used to identify and analyze the most frequently used keywords in academic publications within a specific field, such as agricultural drones. By examining the keywords selected by authors, researchers can gain insights into the main topics and trends in the research area. This analysis helps in understanding the focus of the studies, tracking research directions, and communicating key aspects of the research within the scientific community.
Influential analysis
In the context of the article, the section on influential authors identifies and analyzes researchers with the highest number of citations in the field of agricultural drones. By visualizing author citation networks, the study showcases the impact and influence of these authors within the current literature. The analysis highlights key contributors like L ́opez-Granados F. and Zarco-Tejada P.J., providing insights into the citation structure and impact of their work in the field of precision agriculture and the application of unmanned aerial systems (UAS).
mutual citation analysis
Co-citation analysis is a method used to identify related publications by examining how often two articles are cited together in the reference lists of other publications. By clustering similar studies based on co-citation patterns, researchers can uncover common research themes and intellectual structures within a particular field, such as agricultural drones. This analysis helps to reveal influential and seminal research in the discipline by highlighting connections between different studies based on their citation patterns.
Conclusion of analysis
The conclusion of the study on agricultural drones summarizes the research findings and highlights the significant contributions from countries like the USA, China, India, and Italy, as well as institutions such as the Chinese Academy of Sciences and Texas A&M University. The analysis underscores the importance of advancements in drone technology for precision agriculture, surveillance, and decision-making, emphasizing the potential for integrating drones with other technologies like IoT and big data to optimize agricultural practices and enhance food security monitoring.