SOFTWARE-HARDWARE COMPLEX FOR OBSTACLE SEGMENTATION WITH U-NET ARCHITECTURE FOR AUTONOMOUS AGRICULTURAL MACHINERY

Abstract

Agriculture plays a fundamental role in ensuring food security and meeting the population's
needs for food products. Optimization of agricultural crop production and increasing efficiency
are essential tasks for modern agriculture. In this regard, more attention is being given to the
development and implementation of autonomous agricultural systems capable of automating and
optimizing various production processes. However, the effectiveness of autonomous systems is
limited by the insufficient development of obstacle detection systems and decision-making algorithms.
When agricultural machinery and other autonomous vehicles encounter obstacles in their
path, precise and rapid recognition of these obstacles plays a decisive role in making appropriate
decisions to avoid accidents. This article presents a software-hardware complex for obstacle segmentation using the U-Net architecture, designed to overcome these limitations in autonomous
agricultural systems. The U-Net architecture is renowned for its ability to accurately recognize
objects in images, making it an attractive choice for machine vision systems in agricultural conditions.
The presented complex boasts high performance and enables real-time obstacle segmentation,
including columns, trees, and shrubbery, during the movement of agricultural machinery
along a designated trajectory. This ensures precise decision-making and avoidance of accidents,
significantly enhancing the efficiency and safety of autonomous systems in agricultural production.
Field tests have confirmed the effectiveness and applicability of the proposed solutions under
real agricultural conditions. The presented software-hardware complex with U-Net architecture
opens up new possibilities for autonomous agricultural technology, promoting increased productivity
and efficiency in agriculture. It represents a significant step in the development of modern
agricultural technologies and contributes to the use of autonomous systems to enhance agricultural
production and improve productivity.