ABSTRACT
The fall armyworm (FAW) poses a significant global threat to food security, and economics. Timely detection is crucial, and this research explores innovative techniques like data analysis, remote sensing, satellite imagery, and AI with machine learning algorithms for predicting and managing outbreaks. Emphasizing the importance of community engagement and international collaboration, social network analysis (SNA) is employed to uncover collaborative networks in FAW management research. The study analyzes a decade of research, revealing trends, influential institutions, authors, and countries, providing insights for efficient FAW management strategies. The research highlights a growing interest in Spodoptera frugiperda (Smith and Abbott 1797) research, focusing on biological control, chemical insecticides, plant extracts, and pest resistance. Co-Citation analysis identifies key research concepts, while collaboration analysis emphasizes the contributions of actors and institutions, such as China, the USA, and Brazil, with international collaboration playing a vital role. Current research trends involve evolving resistance, insecticidal protein gene discovery, and bio-control investigations. Leveraging insights from collaborative networks is essential for formulating effective strategies to manage fall armyworm and ensure global food security. This comprehensive analysis serves as a valuable resource for researchers and stakeholders, guiding efforts to combat this pervasive agricultural pest.
