Aligning process quality and efficiency in agricultural soil tillage

Automation in agricultural machinery is a crucial driver of productivity and sustainability. Some automation features like automated steering and real-time data analytics are already state-of-the-art. On the other hand, a human driver performs the optimization of the working speed manually, and the automation of this is an ongoing challenge. Process quality and process efficiency are the two main targets in this optimization. Agricultural soil tillage requires achieving both. Therefore, the correlation between process quality optimization and process efficiency is fundamental, and vice versa. The approach presented in this paper shows how the two optimization targets of efficiency and process quality can be optimized and aligned together. Optical sensors determine various parameters to describe and model the process quality. The measured machine state determines the characteristics of the interaction forces between the machine and the environment. A machine learning algorithm describes the relationships in the drivetrain. The two process targets are each predicted for different working speeds and are combined in the form of a boundary target and an optimization target to identify one optimized target speed value.