Abstract Accurately detecting and localizing vineyard disease detections are essential to reduce production losses. A great variety of scientific work focuses on remote sensing methods, while with current learning-based techniques, a continuous paradigm shift is happening in this domain. Based on a thorough literature review, the need for a remote assistance survey for the detection of vine disease was motivated by the adoption of recent machine learning algorithms. Thus, in this work, the research outputs from the past few years are summarized in the domain of grapevine disease detection. A remote sensing-based distance taxonomy was introduced for different categories of detection methods. This taxonomy is relevant for differentiating among the existing solutions in this domain, the resulting methods being grouped according to the proposed taxonomy. The articles and public datasets cited are collected on the website of this project (https://molnarszilard.github.io/VinEye/).