Design and Development of a Versatile Drone for Multi-Payload Applications

The paper presents the innovative design and development of a versatile, unmanned aerial vehicle (UAV) optimized for multi-payload applications, with an emphasis on agricultural crop disease detection. The drone is equipped with a variety of sensors such as high-definition video cameras, gyroscopes, and GPS, which makes it easier to integrate different payloads customized for particular applications. An important aspect of the proposed UAVis its integration of machine learning technology, specifically a Convolutional Neural Network (CNN), for the real-time analysis and identification of crop diseases from aerial images. This method promises to revolutionize agricultural practices by enabling precise, early detection of diseases, thereby minimizing crop losses and optimizing yields. The paper provides the construction of the drone, including material selection for durability and weight optimization. It also outlines the machine learning pipeline, from data collection and model training to real-time deployment on the drone for in-field analysis. The results demonstrate the UAV's capability to successfully identify crop diseases, which shows its potential for broader applications in surveillance, delivery, and environmental monitoring. YOLO (You Only Look Once) is an advanced object detection algorithm renowned for its real-time performance and high accuracy. Unlike conventional methods that detect objects in multiple stages, YOLO employs a single convolutional neural network (CNN) to simultaneously predict bounding boxes and class probabilities. This approach enables rapid and precise object detection in images and videos, making it ideal for applications requiring fast processing, such as surveillance, autonomous vehicles, and drone operations.