Soil compaction caused by heavy agricultural machinery poses a significant challenge to sustainable farming by degrading soil health, reducing crop productivity, and disrupting environmental dynamics. Field traffic optimization can help abate compaction, yet conventional algorithms have mostly focused on minimizing route length while overlooking soil compaction dynamics in their cost function. This study introduces Soil2Cover, an approach that combines controlled traffic farming principles with the SoilFlex model to minimize soil compaction by optimizing machinery paths. Soil2Cover prioritizes the frequency of machinery passes over specific areas, while integrating soil mechanical properties to quantify compaction impacts. Results from tests on 1000 fields demonstrate that our approach achieves a reduction in route length of up to 4-6% while reducing the soil compaction on headlands by up to 30% in both single-crop and intercropping scenarios. The optimized routes improve crop yields whilst reducing operational costs, lowering fuel consumption and decreasing the overall environmental footprint of agricultural production. The implementation code will be released with the third version of Fields2Cover, an open-source library for the coverage path planning problem in agricultural settings.

Reducing product damage, preserving quality, and enhancing efficiency from harvest to consumption are crucial for sustainable agriculture. The integration of advanced information and communication technologies into agricultural practices plays a vital role in meeting these goals. This study introduces an autonomous transport vehicle designed for the efficient logistics of fruit transportation in agricultural settings. The vehicle's software framework is constructed on the Robot Operating System (ROS) and incorporates an enhanced hybrid navigation system that merges the Extended Kalman Filter (EKF) with Simultaneous Localization and Mapping (SLAM) for precise localization. The A* algorithm facilitates global path planning, whereas the Dynamic Window Approach (DWA) guarantees real-time obstacle avoidance. Essential hardware components comprise high-resolution LIDAR for environmental mapping, an Inertial Measurement Unit (IMU) for motion estimation, and wheel encoders for odometry. The performance evaluation was executed across five distinct terrain types: concrete, fine-tilled soil, coarse-tilled soil, asphalt, and grass. The vehicle attained optimal path-following precision on concrete, exhibiting a deviation of 5.39 cm at a speed of 0.3 m/s with a 200 kg payload, whereas tracking errors escalated on uneven terrains like grass and coarse-tilled soil. Maneuverability assessments verified a turning radius of 60.0 cm for 90 degrees turns and 125.0 cm for 180 degrees turns, ensuring suitability in restricted agricultural environments. Finite element analysis (FEA) evaluated structural durability under diverse loads (2000-4000 N), indicating a minimum safety factor of 1.23, thereby affirming structural stability under static conditions. This study demonstrates the potential of autonomous transport vehicles to revolutionize agricultural logistics by reducing labor dependency, improving operational efficiency, and supporting sustainable farming.