# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 1 Introduction  
The agricultural industry has recently undergone significant reconstructions in both field and protected agriculture [1]. Alternative energy sources are increasingly replacing fossil fuels in machinery [2] due to the effects on the environment from agricultural emissions. Additionally, migration from rural to urban areas is accelerating [3, 4], indicating that effective labor is decreasing, and the climate crisis is driving the development of greenhouse technology [5]. The aging farming population [6] further highlights the importance of robotic technology in agriculture. Given that farming poses physical [7, 8] and mental risks [9], the trend of declining rural populations [10] is likely to continue. Consequently, experts are increasingly focusing on automated and digital agriculture [11]. Protected agriculture offers significant advantages, primarily because it is less influenced by external factors, such as the climate crisis, compared to field agriculture [12, 13]. Advanced technologies allow for flexible responses and adaptations. For example, strategies for optimizing indoor environments [14] and wireless data acquisition systems using IoT and sensors enable remote monitoring services for farmers [15]. Despite these advancements, the accessibility of developed technology is not appropriate for all countries, onlythe advanced nations [16; 17]. Also, the development of fully automated systems remains an ongoing challenge with the integration of advanced sensing technologies, the quality of intelligent decision-making algorithms, and the precision of automation [16]. While advanced technology in protected agriculture is successfully reducing the need for human labor, field agriculture still lags behind due to precision challenges. The irregular terrains in farmland increase the complexity of determining the recognition and manipulation of crops [18], and the poor knowledge of and lack of interest in current technologies of aging farmers also hinder their advancement of agriculture [19]. These challenges affect every step of field agricultural operations: (1) fertilizing, (2) rotary tilling and soil disinfection, (3) ridge forming, (4) planting, (5) management, (6) harvesting, and (7) transport. In detail, variable-rate fertilizer application shows fluctuating coefficients of variation, indicating a need for machinery redesign [20]. Constructing datasets with qualified data requires long periods of time, and the uncertainty of achieving it always exists [21]. Task allocation systems for robotic spraying have demonstrated reliable performance but still face collision risks, necessitating safety measures [18]. Robots in field agriculture also play crucial roles in vision and monitoring. They can oversee the location and density of planted seeds during the initial stages [22]. Vision systems often require Lidar sensors to detect unspecified obstacles [23]. However, these systems face endurance and precision challenges, particularly in detecting numerous obstacles and maintaining performance when working outdoors, where thermal issues can arise. For the harvest, the use of costly articulated arm and multi-joint robots for picking crops, coupled with computers and cameras for autonomous driving and AI technology for crop identification, has demonstrated the potential for automation. However, improvements in speed and quality are still needed [24; 25]. Despite these challenges, agricultural automation is expected to be achieved through multi-robot systems, where robots collaborate and share data on soil, environment, and field conditions. This collaboration will enable a more flexible and scalable field [26]. Consequently, time-consuming tasks, such as data collection and processing, and predictable physical works can be replaced by achieving agricultural automation [27]. Then, fully automated agriculture can be accomplished in the areas of preparation, cultivation, and management [28].