Control of a neural network for the operation of small aircraft for agricultural purposes

Abstract

We will discuss the impact of various parameters, such as network size and complexity, on accuracy, speed, and efficiency, as well as ways to optimize them. We also analyze the use of deep learning algorithms to detect and classify objects and adjust the navigation of small agricultural aircraft depending on the environment. We also highlight the importance of data preprocessing and how it can be used to improve the accuracy and speed of deep learning algorithms. Finally, we will discuss how deep learning can be used to automate certain tasks, such as detecting and repairing leaks or anomalies, as well as the use of aircraft from other fields, in the field of agriculture.