Traditional methods for controlling multi-rotors typically involve joysticks, radio controllers, and mobile applications. However, these methods pose significant challenges, particularly for novice users like farmers, due to the extensive training and understanding required to effectively operate a copter. This paper introduces a highly adaptable architecture designed to offer an end-to-end solution for controlling a copter using hand gestures. The proposed system leverages a depth sensor and Convolutional Neural Network (CNN) to recognize hand gestures, utilizing a custom dataset collected from both indoor and outdoor environments.Through a series of simulations with novice users, the system has demonstrated successful operation in real-world scenarios. Currently, the architecture can accurately recognize six distinct gestures with an average accuracy of 90.5% across three different test environments with varying lighting conditions. Key features of this proposed solution include its adaptability, reliable performance, especially in low-light conditions, and its user-friendly design, making it particularlywell-suited for farmers and other inexperienced users.
Hussain et al. (Sat,) studied this question.