Research progress and technical pathways of deep learning in agricultural remote sensing: a bibliometric and Task–Data–Model analysis

Deep learning has rapidly expanded in agricultural remote sensing, but its thematic evolution and technical configurations remain insufficiently examined together. This study analysed 1,706 Web of Science Core Collection articles published from 2017 to 2025 using CiteSpace and VOSviewer, and applied Task–Data–Model (T–D–M) coding to a publication-year-stratified random sample of 500 articles. Publications increased markedly after 2020, with broader collaboration networks and interdisciplinary participation. Four major themes were identified: crop growth and yield prediction, land-cover classification and segmentation, object-level recognition and fine-scale monitoring, and broader deep-learning applications. Temporal analyses showed growing attention to time-series modelling and multi-source integration. In the coded sample, Task–Data association was strongest (Cramér’s V = 0.362), exceeding Task–Model (0.215) and Data–Model (0.186). These results show that data-source selection is more task-structured than broad model-family use.