A mobile inspection platform based on multi-dimensional perception was developed to enable intelligent inspection and monitoring of maize growth dynamics, drought stress and diseases in wide fields. Firstly, the chassis assembly’s steering system, drive system, and control system were developed, and the steering and driving functions of the inspection platform were implemented, using the Arduino UNO controller. Secondly, a multi-dimensional sensing system that consisted of a global navigation satellite system/inertial navigation system (GNSS/INS) integrated navigation system, light detection and ranging (LiDAR) and camera was constructed. The time synchronization scheme, data communication structure and information acquisition software of the sensing system were then designed to enable the patrol platform to perceive its environment and visualize. Finally, the chassis driving performance test and the perception system environment perception test were performed on the inspection platform in the maize field. According to the test results, the inspection platform’s average minimum turning radius for left turns was 2922mm, its average minimum turning radius for right turns was 2736mm, and its maximum climbing gradient was greater than 26.7%, the average straight-line speed under position PID control was 0.523m/s, with an error of 4.6% compared with the expected speed of 0.5m/s, the average deviation for 15m driving was 0.636m, and the average deviation rate was 4.24cm/m, all of which met the field driving performance requirements. Under the ROS system, the sensing system was capable of reliably gathering platform position information, high-precision 3D point cloud information, and color 2D image information, enabling the inspection platform to perceive the surroundings in many dimensions. The research result can be used to guide the intelligent creation of a maize field inspection platform.