Research Progress on Intelligent Detection of Plant Diseases and Pests: A Bibliometric Analysis Via Bibliometrix

This study conducts a comprehensive bibliometric analysis of 233 publications from 2016–2025 retrieved from the Web of Science Core Collection, aiming to map the global research landscape of intelligent detection of plant disease and pest. Using Bibliometrix and related tools, the study examines publication trends, leading countries and institutions, collaboration networks, and thematic structures. Results show a continuous growth in research output, with China, the United States, and India emerging as major contributors, and China leading in both productivity and citation impact. Keyword co-occurrence and thematic mapping reveal deep learning, convolutional neural networks, transfer learning, and object detection as central research themes, while remote sensing, drones, and smart farming represent rapidly developing fronts. Burst-term analysis further highlights lightweight models, vision transformers, and real-time detection as emerging technological directions. This study provides valuable insights into the knowledge evolution, research hotspots, and future pathways of AI-driven plant disease and pest detection.