 The use of unmanned aerial vehicles, UAVs, has become increasingly popular in precision agriculture due to their ability to provide high-resolution images of crops. These images can be used to detect crop diseases early, which is important for preventing potential loss of crop yield. Machine learning and deep learning techniques are being used to improve the accuracy of these detection systems by analyzing the images and extracting relevant features from them. This paper reviews the current state of the art in UAV-based remote sensing for crop disease detection, including sensor selection and image processing techniques, as well as the performance of various machine learning and deep learning algorithms. The authors also discussed the challenges and opportunities associated with this technology, as well as future research directions. This article was authored by Tej Bahadur Shahi, Chinyuan Su, Arjun Nupain, and others.