From Molecules to Fields: Mapping the Thematic Evolution of Intelligent Crop Breeding via BERTopic Text Mining

The convergence of agricultural biotechnology and artificial intelligence is reshaping modern crop improvement. Despite a surge of studies integrating artificial intelligence and biotechnology, the rapidly expanding literature on intelligent crop breeding remains fragmented across molecular, phenotypic, and computational dimensions. Existing reviews often rely on traditional bibliometric or narrative approaches that fail to capture the deep semantic evolution of research themes. To address this gap, this study employs the BERTopic model to systematically analyze 1867 articles (1995–2025, WoS Core Collection), mapping the thematic landscape and temporal evolution of intelligent crop breeding and revealing how methodological and application-oriented domains have co-evolved over time. Eight core topics emerge, i.e., (T0) genomic prediction and genotype–environment modeling; (T1) UAV remote sensing and multimodal phenotyping; (T2) stress-tolerant breeding and root phenotypes; (T3) ear/pod counting with deep learning; (T4) grain trait representation and evaluation; (T5) CRISPR and genome editing; (T6) spike structure recognition and 3D modeling; and (T7) maize tassel detection and developmental staging. Topic-evolution analyses indicate a co-development pattern, where genomic prediction provides a stable methodological backbone, while phenomics (UAV/multimodal imaging, organ-level detection, and 3D reconstruction) propels application-oriented advances. Attention dynamics reveal increasing momentum in image-based counting (T3), grain quality traits (T4), and CRISPR-enabled editing (T5), alongside a plateau in traditional mainstays (T0, T1) and mild cooling in root phenotyping under abiotic stress (T2). Quality stratification (citation quartiles, Q1–Q4) shows high-impact concentration in T0/T1 and a growing tail of application-driven work across T3–T7. Journal analysis reveals a complementary publication ecosystem: Frontiers in Plant Science and Plant Methods anchor cross-disciplinary dissemination; Remote Sensing and Computers and Electronics in Agriculture host engineering-centric phenomics; genetics/breeding journals sustain T0/T2; and molecular journals curate T5. These findings provide an integrated overview of methods, applications, and publication venues, offering practical guidance for research planning, cross-field collaboration, and translational innovation in intelligent crop breeding.