As the development of electric tractors progresses, battery systems have become a key component, accounting for a significant portion of the vehicle’s total weight. With rollover accidents remaining a leading cause of fatal injuries in agricultural machinery, the stability of electric tractors is drawing increasing attention. In particular, battery placement may critically affect the overall mass distribution and rollover behavior, highlighting the need for safety-focused design optimization. This study evaluates the static rollover stability of a 55 kW electric tractor by analyzing the effect of battery mounting position and comparing it with a conventional tractor. Three tractor models were considered: an electric tractor with a front-mounted battery, one with a center-mounted battery, and a conventional tractor. Multibody dynamic simulations were conducted using RecurDyn, and a total of 24 orientations, at 15° intervals, were simulated to determine the tipping angles in all directions. The results revealed that battery placement had a significant impact on rollover stability. The front-mounted battery type exhibited up to 30% higher tipping angles than the conventional tractor in the forward pitch direction near 90°, indicating improved stability. In contrast, the center-mounted battery type showed a tipping angle distribution generally similar to that of the conventional tractor, with smaller variations across directions. These findings demonstrate the influence of mass distribution on rollover safety and provide valuable insight for structural design of electric tractors.

The decreasing rural population and migration to urban areas for high-tech opportunities have weakened the agricultural labor force. While data technology has been adopted in protected agriculture, numerous challenges remain in field agriculture. In this study, we focus on one of the fundamental steps of field operations, i.e., ridge forming, specifically in unmanned agriculture. We compared the performance of a conventional tractor with an implement to that of a ridge-forming robot. The operation data were collected using an acquisition system, and a comparison between the results of both methods was conducted. Additionally, we analyzed the linearity of autonomous driving and the expenses associated with the selected operation. Our findings indicate that the developed robot for ridge forming caused less torque damage and achieved a more accurate target soil depth, with a linearity performance showing a distance error of only 0.267 m. Furthermore, it eliminated the need for hiring an operator and significantly reduced fuel consumption, which accounts for 50.81% of the operational expenses. These results suggest that field operations can be effectively replaced by autonomous systems, and further research on unmanned agriculture is warranted.

Manure distribution in soil creates a ground environment that is conducive to crop cultivation. However, the lumping and concentration of manure in the field can occur, hindering the fertilization of the soil for plant growth, and the randomization of nutrients under different soil depths accelerates it. To overcome the challenges associated with agricultural testing, such as high cost, inclement weather, and other constraints, computational analysis is often used. In this study, rotary operations are performed using the discrete element method (DEM) to ensure the uniform distribution of manure and four soil layers. DEM analysis was conducted with three experimental factors, and simulation sets were designed using the Box-Behnken central combination method. The DEM results were evaluated using the uniformity index (UI), and the field test of the rotary operation was performed with the set showing the most uniform distribution among the results. Due to undistinguishable particles in reality, the uniformity was validated by a comparison of the chemical characteristics of the L₁ and L₅ in terms of before and after the rotary operation. The DEM parameter of the soil was determined by performing field measurements at different soil depths (0–20 cm), and this parameter was calibrated by conducting a penetration test. The Box–Behnken central combination method was implemented using the following factors: tillage depth (X₁), PTO revolution speed (X₂), and forward machine velocity (X₃). These factors were obtained using the UI regression model and the response surface method. In the results, it was indicated that the UI was affected by the factors in the following order: X₁ > X₂ > X₃. The optimized factor values were X₁ = 25 cm, X₂ = 800 RPM, and X₃ = 1.8 km/h, leading to a UI of 6.07, which was consistent with the analysis results. The operating parameters were maintained throughout the field test, and the acquired data were input into the measurement system. The lowest UI value of 6.07 had the strongest effect on decreasing the disparity between L₁ and L₅, especially in terms of pH, organic matter, P, Ca, and Mg. In summary, the results indicated that soil distribution can be controlled by adjusting mechanical parameters to ensure uniform chemical characteristics across various soil depths.