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.