Synthetic Data Generation for Tasks of Recognizing Actions and Interaction Objects of an Agricultural Drone Operator

This paper presents a prototype application for generating synthetic data used to train neural network models for recognizing actions and interaction objects of an agricultural drone operator. This application has been implemented with the ability to customize various generation parameters. The obtained generation results are saved as images and text files in YOLO format.