Costa, Diogo SantosBarriguinha, AndréAreosa, InêsCaetano, MárioTeixeira, IgorVanneschi, Leonardo2026-02-112026-02-112026-032772-3755PURE: 150781074PURE UUID: 8b8082f0-3ad7-4f84-86d6-12d4cc1e4f8fScopus: 105034288911WOS: 001683164400002http://hdl.handle.net/10362/200294Costa, D. S., Barriguinha, A., Areosa, I., Caetano, M., Teixeira, I., & Vanneschi, L. (2026). Remote sensing-based approaches for automatic vineyard area identification: a systematic review. Smart Agricultural Technology, 13, Article 101812. https://doi.org/10.1016/j.atech.2026.101812 --- This research was supported by national funds from the Portuguese Foundation for Science and Technology (FCT), I.P., under the call Artificial Intelligence, Data Science and Cybersecurity relevant to Public Administration, project 2024.07551.IACDC/2024 - AIGODS (Artificial Intelligence Grapevines Overwatch and Detection System). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UID/04152/2025 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS - https://doi.org/10.54499/UID/04152/2025 (2025-01-01/2028-12-31) and UID/PRR/04152/2025 https://doi.org/10.54499/UID/PRR/04152/2025 (2025-01-01/ 2026-06-30)"Background Sustainable vineyard management and planning require reliable methods for identification and monitoring. This systematic review synthesises and appraises the literature on automatic vineyard identification using remote sensing (RS), from classical techniques to artificial intelligence (AI), describing the state of the art, patterns, challenges, and gaps. Methods Guided by PRISMA and informed by selected SWiM reporting items, we conducted a systematic search across multiple databases, gathering all relevant records up to 13 July 2025, and included 108 sources, of which 80 empirical studies contributed to the synthesis. The risk of bias was assessed by adapting the principles of PROBAST-AI and QUADAS-2 to the agricultural context, covering data representativeness, sensors/pre-processing, ground-truth, validation, and portability; its application also guided the selection and organisation of the synthesis. Results The analysis was narrative and structured by scale and application objective (regional, parcel, row, and plant). The most common tasks were classification (28%), detection (26%), and segmentation (24%), with multitask pipelines being frequent. We observe a clear transition from pixel-based approaches using satellite imagery to methodologies that integrate very-high-resolution UAV imagery, 3D reconstruction, and Deep Learning (DL). UAVs dominate row and plant-level analyses, whereas Sentinel-2 has become the main tool for multitemporal regional monitoring. DL models, such as CNNs and Vision Transformers (ViTs), tend to deliver superior performance in canopy segmentation and parcel classification. The assessment identified model validation as the weakest methodological domain across studies. Discussion The main limitations lie in weak spatiotemporal portability of models and high computational costs, aggravated by reliance on large volumes of annotated data. Promising directions include multisensory fusion (UAV + satellite) and the integration of 3D information into DL pipelines, which increase robustness and operational applicability. These advances are enabling high-value, specialised objectives such as mapping in complex terrain, detecting abandoned vineyards, and identifying missing plants.215496578engprecision viticultureremote sensingarea identificationunmanned aerial vehiclessatellitepixel-baseddeep learningsystematic literature reviewComputer Science (miscellaneous)General Agricultural and Biological SciencesArtificial Intelligencereview10.1016/j.atech.2026.101812https://www.scopus.com/pages/publications/105034288911https://www.webofscience.com/wos/woscc/full-record/WOS:001683164400002https://data.mendeley.com/datasets/mjrhkf57ms/2