Detection and monitoring wheat diseases using unmanned aerial vehicles (UAVs)

Wheat is a major grain crop cultivated worldwide, and its production is influenced by various diseases and pests, leading to significant production losses. Timely disease detection in wheat is crucial for farmers to apply effective control measures and prevent disease spread and potential reduction of yield and quality. However, conventional disease detection typically demands trained professionals and sophisticated laboratory equipment for accurate identification, making it economically unfeasible for large-scale wheat cultivation. In recent years, various research efforts have focused on finding alternative methods for disease detection in wheat using Unmanned Aerial Vehicles (UAVs) in conjunction with deep learning and image processing techniques. Nevertheless, there is a notable absence of comprehensive studies that review and compare these various approaches. Our article seeks to bridge this gap by providing a comparative analysis of different types of UAVs, sensors, image processing methods, and classification techniques employed in wheat disease detection. Additionally, we delve into the opportunities and challenges associated with the use of UAVs as tools for disease monitoring in wheat cultivation. Our review shows the potential of UAVs for automated disease detection and monitoring in wheat.