Pitch Angle Compensation and System Design for Tillage Depth Monitoring of Mounted Moldboard Plough

Ploughing constitutes a fundamental tillage practice for enhancing soil fertility as well as water and fertilizer utilization efficiency. The uniformity of tillage depth directly determines seedling emergence consistency and final crop yield. Conventional indirect tillage depth detection approaches based solely on the rotation angle of the tractor lift arm are susceptible to disturbances induced by uneven terrain and dynamic attitude fluctuations of the tractor-implement system. To compensate for disturbances induced by tractor pitch angle variations, existing pitch compensation models adopt cascaded output compensation to calculate tillage depth. Nevertheless, these models need recalibration after each modification to the three-point hitch linkage length and are incapable of simultaneously compensating for pitch variations in the tractor and attached implement. To reduce the sensitivity of the tillage depth model to structural changes in the three-point hitch, this work constructs a mapping between hitch structural geometric deviations and vertical projection offsets of key linkages via tractor–plough integrated distributed attitude sensing. In this paper, a distributed tillage depth model is developed, which takes the distributed attitude of the whole working unit and the pitch of the lower-link measured by a rotation angle sensor as independent input variables. Field validation was performed with a mounted moldboard plough, with ultrasonic ranging sensor tillage depth readings serving as the reference benchmark to assess the reliability and precision of the proposed distributed model. Experimental results show that compared with the pitch angle cascaded tillage depth model, the average root mean square error of the proposed method is reduced from 22.5 mm to 12.5 mm on bumpy fields and from 11.3 mm to 8.7 mm on flat fields. The distributed model can significantly improve the measurement accuracy and anti-interference capability of tillage depth detection on uneven farmland, and it is applicable to adaptive measurement and control of tillage depth in complex ploughing scenarios.