Deep Learning for Transformer-Based Plant Disease Detection: A Bibliometric Analysis

Agriculture, food security, and economic stability are impacted by plant diseases, making their identification and diagnosis essential. This article illustrates the research trends in plant disease detection using transformers through a bibliometric analysis based on visualization. The publications used in this work were collected from Scopus and Web of Science databases. For visualization, programs such as Biblioshiny and VOSViewer 1.6.20 were used. The results demonstrate that China is the most productive country, accounting for 11 total publications and 126 citations. China Agricultural University was the most productive institute, with six publications, while the Frontiers in Plant Science journal was the most productive journal, with six publications and 102 citations. It also demonstrates that the most used research topics in this field are “deep learning”, “plant disease”, and “vision transformer”. This study provides insights into the application of transformers for plant disease detection, enabling researchers to better understand and explore this field.