The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control platform for farm location information acquisition is time-consuming, labor-intensive, and lacks the whole process control of multiple types of farm machinery. In this paper, we propose an Internet of Things (IoT) control scheme for intelligent farm machinery operation of unmanned farms and design the access standards for multiple types of farm machinery, as well as realize the remote control of intelligent farm machinery operation by constructing a remote control model. A high-precision map construction method is designed to improve the DeepLabV3+ algorithm to identify fields and roads. The control models of path planning, remote task, remote control, and safety system are built to achieve the remote control of intelligent agricultural machinery operation. The proposed technology is implemented in the platform integration and application tests are carried out. The error of the constructed high-precision map is less than 3 cm, the completeness rate of the automatic boundary extraction rate is 96.71%, and the correctness rate is 95.63%, which can be used to obtain the boundary instead of manual labeling or on-site point picking. The use of the platform for the simultaneous control of three farm machinery operations reduces the number of people in operation and production and reduces the professional requirements of the personnel, which will promote the management of the entire farm by one person or even by no one in the future.

Aiming at the application environment of paddy agricultural machinery with bumpy and undulating changes, the problems affecting the method for steering wheel angle measurement by MEMS gyroscope were analyzed, and a wheel angle measurement method combining Dual-MEMS gyroscope (dual MEMS gyroscope) and RTK-GNSS was designed. The adaptive weighting method was used to fuse the heading angle differentiation of RTK-GNSS, the MEMS gyroscope angle rate, and velocity data, and the rod-arm compensation was performed to accurately obtain the angle rates of the body and steering wheels of agricultural machinery; the difference between the combined angular rate of the steering wheel of the agricultural machinery and the angular rate of the agricultural machinery body was obtained, and the integrator is used to integrate the difference to get the wheel steering angle value, and the Kalman filter was designed to make feedback correction for the integration process of angle calculation to eliminate the errors caused by the gyroscope zero bias, random drift, and gyroscope rod arm effect, and to obtain the accurate value of wheel steering angle. A comparative test with the connecting rod wheel angle sensor was designed, and the results show that the maximum deviation is 4.99 degrees, the average absolute average value is 1.61 degrees, and the average standard deviation is 0.98 degrees. The method in this study and the connecting rod wheel angle sensor were used on paddy farm machinery. The wheel angle measurement deviation of the proposed method and the connecting rod wheel angle sensor was not more than 1 degrees, which is relatively small. It has good stability, speed adaptability, and dynamic responsiveness meets the accuracy requirements of steering wheel angle measurement for paddy field agricultural machinery unmanned driving and can be used instead of connecting rod angle sensors for unmanned agricultural machinery.

The annual rice planting area in China is approximately 30 million ha. With continuous reductions in the rural labour force and increasing production costs, it is urgent to develop unmanned technology for paddy field agricultural machinery to solve the problem of "who will farm the land". In paddy field environments with slippery mud and uneven hard underlying, side slip and slip of paddy farm machinery can easily occur. Due to the relative movement and attitude changes between paddy farm machinery and implements and the inconsistent driving track of the farm machinery and implements, it is difficult to track and control the path of unmanned paddy farm machinery. In view of the above problems, this paper takes the paddy field agricultural machinery body as the control object and the agricultural machinery pose as the observation quantity and establishes an agricultural machinery kinematics model based on agricultural machinery pose correction. Based on the model and model predictive control (MPC), the linear model, objective function and constraint function of paddy field agricultural machinery are designed, an MPC path tracking control method is established based on the pose of the agricultural machinery, and field experiments are conducted. The results show that the average root mean square error of the three-line straight-line path tracking is 0.043 m, the average absolute error is 0.033 m, and the effect is favourable. The MPC path tracking method based on the pose correction of agricultural machinery can effectively suppress abrupt lateral position deviations caused by the relative position and attitude changes of the machine, can improve the control accuracy, and can meet the control accuracy requirements of unmanned paddy field agricultural machinery operations.