## 1 Introduction  
The global agricultural landscape has undergone significant changes in recent years, driven by rising environmental concerns and the evolving needs of a more health-conscious population. As consumer awareness about sustainable farming and the health benefits of organically grown produce has increased, there has been a parallel rise in interest toward improving agricultural systems that support environmentally friendly practices [1, 2, 3, 4, 5, 6, 7, 8]. Climate change has also introduced significant challenges, altering growing conditions and putting strain on existing farming practices, which necessitates resilient farming solutions [3]. In the Republic of Korea, the agricultural sector faces additional challenges due to the decreasing and aging workforce, leading to a significant labor shortage [9]. This shortage directly impacts agricultural productivity and sustainability, emphasizing the urgent need for automation and mechanization in farming practices. Furthermore, the country's compact and rugged terrains, characterized by narrow and confined furrows between crop rows, limit the effectiveness of traditional large-scale agricultural machinery. As a result, there is a growing market need for innovative solutions that can enhance productivity while adapting to these unique geographical constraints. These dynamics have emphasized the importance of smart, adaptable technologies that support sustainable practices while minimizing environmental footprints [8]. Recognizing this market demand, our research project aims to develop small mobile robots specifically designed for transporting crops in challenging outdoor environments. Small mobile robots have emerged as a promising solution to address both labor shortages and the limitations posed by the terrain. They offer specific advantages over traditional machinery, such as reducing soil compaction, enhancing energy efficiency, and operating flexibly in confined spaces. This is particularly crucial in the Republic of Korea, where mobility in narrow spaces is essential for efficient farming operations. Technological advances in autonomous navigation and machine learning have enabled the development of agricultural robots with enhanced efficiency and precision. These robots are now capable of operating effectively in complex outdoor environments. Innovations in LiDAR-based perception, vision systems, and multi-modal data integration are key to enhancing robots' ability to navigate farm paths effectively [2; 6]. Research has shown that these technologies can significantly reduce labor-intensive tasks and increase productivity in precision farming [3; 6]. Consequently, with a growing emphasis on reducing labor needs particularly in countries like in the Republic of Korea, robotics has emerged as a promising solution [7]. Figure 1 illustrates examples of crop transportation equipment in the Republic of Korea, highlighting the need for improved mobility and automation. This study utilizes a low-spec 2D LiDAR sensor to develop a system capable of effectively following objects in close proximity within an outdoor agricultural setting, aiming to enhance the practicality of small mobile robots in such environments. With the recent advancements in machine learning and robotic systems, recognition and classification technologies of human forms utilizing various sensor data have become important research areas. In this context, two-dimensional (2D) LiDAR (Light Detection and Ranging) has emerged as a widely used technology in various applications such as personal and industrial robots and autonomous vehicles due to its advantages of being relatively inexpensive and capable of sensing a wide range. This technology excels at quickly scanning and mapping the surrounding environment and is essential for recognizing dynamic objects like humans in safety-related industries [10; 11; 12; 13; 14; 15; 16]. Figure 2 illustrates various applications of LiDAR sensors, demonstrating their versatility and importance in modern robotic systems.