Aiming to address the problems of low detection accuracy and poor stability due to the weak anti-interference ability of the bridge circuit and operational amplifier circuit, and the influence on the capacitance of the bulk density and temperature of the straw bale, an on-line detection device for the moisture content of straw bales in a square baler was developed based on the capacitance method. The device integrates a capacitance sensor, pressure sensor, and temperature sensor. The optimal structure size of the capacitor plate was determined through the simulation test of the capacitor sensor plate structure. A moisture content monitoring system based on the MATLAB language is built, and the moisture content detection model was constructed based on the backpropagation neural network (BPNN) algorithm. Finally, a test bench for a square baling machine was designed, and a performance verification test of the moisture content detection device was carried out. The simulation results of the capacitor plate show that when the length, width, and spacing of the capacitor plate are 148.6, 47.7, and 5.1 mm, respectively, the detection sensitivity of the capacitor plate is the highest. The modeling results show that the R², RMSE, and RPD of the BPNN model are 0.986, 0.008998, and 5.99, respectively, with solid data fitting ability and high prediction accuracy. The bench test results show that for the samples having moisture content between 13.1 and 28.04%, the coefficient of determination R² of the fitting curve between the predicted value of moisture content and the actual value is 0.949. The relative error range of the predicted value of moisture content is −6.51–8.66%, and the absolute error range is −1.63–1.72%. The on-line detection device for moisture content of straw bales has good accuracy and stability.

Complex unstructured factors, such as the spatial heterogeneity of soil mechanical properties and random variations in farmland topography, cause fluctuations in working load, resulting in low efficiency and poor quality of subsoiling operations. To address this, this study developed a combined speed–tillage depth control system for power-shift tractors based on working load adjustment. Based on dynamics theory, a six-degree-of-freedom dynamic model of the tractor subsoiling unit was established. Through simulation experiments, the working quality of the combined speed–tillage depth control system was evaluated under different working speeds, tractor gear positions, and traction resistance conditions. Simulation results indicated that under stable working conditions, the speed control error was <0.03 km/h; when the increase in traction resistance was less than 4000 N, the speed control error was less than 0.19 km/h; and when the increase in traction resistance exceeded 4000 N, the speed control error was <0.53 km/h. Field test results indicated that the speed control error was less than 0.6 km/h, the tillage depth control error was less than 4 cm, and the slip was less than 0.25. The coordinated speed–tillage depth control system developed in this study enables precise control of speed and tilling depth under dynamic operating conditions.