The use of unmanned aerial vehicles (UAVs) and image-processing techniques has revolutionized disease detection and control in field crops. This essay aims to explore the potential of UAVs and image-processing techniques in monitoring and managing plant diseases, particularly in large-scale crops. Several studies have demonstrated the efficacy and accuracy of using UAVs to capture high-resolution images that can be processed using machine learning algorithms to detect and classify crop diseases. The utilization of UAVs in crop disease management can significantly reduce labor costs and improve the accuracy and timeliness of disease detection. In addition, UAVs can be equipped with precision sprayers that can apply fungicides and insecticides to diseased areas in a targeted manner, reducing the amount of chemicals used in crop treatments and minimizing the environmental impact. However, despite the potential benefits, several challenges remain in realizing the full potential of UAVs in crop disease management. These challenges include regulatory and legal barriers, issues with data storage and analysis, and technical limitations of UAVs. Nevertheless, the use of UAVs and image-processing techniques remains a promising approach that has the potential to transform the way we monitor and manage crop diseases. The paper explores techniques for identifying plant diseases through leaf images and segmentation and feature extraction algorithms employed in the detection process.