The complex operating environment of agricultural quadruped robots causes them to fall easily when walking in the field, which affects the operating efficiency of the robot, and accurate prediction of the body fall state is of great significance to the walking stability of the robot. A critical state prediction method for robot fall was proposed based on ontology sensor signal processing. Firstly, the inertial measurement sensor signals of the quadruped robot walking and falling in a corn field and the fall state of the robot during field walking simulated by Gazebo software were collected, and the signals of the robot’s normal walking, the two phases of the critical stable state of falling and the four working conditions of complete falling were classified to generate signal datasets of different body states. Secondly, a population optimisation algorithm was used to optimize the parameters of variational mode decomposition (VMD), and an improved population optimization variational mode decomposition ( IPO VMD) algorithm was proposed. And IPO algorithm was adopted to optimize the parameters of general regression neural network (GRNN), and improved population optimization general regression neural network (IPO GRNN) was proposed. Finally, based on the above signal processing method, a fall prediction method for field operation robots based on the IPO VMD GRNN model was established, and the signals of the traverse roll and pitch attitude angle of the robot’s actual field walking were used as the model test data to verify the performance of the fall prediction model for field operation robots. The test results showed that the IPO VMD GRNN model outputed a total error of 0.146 7, an average relative error of 0.006 5, and a mean square error of 0.000 3, and the extracted features were well represented;compared with the VMD BPNN, VMD GRNN, and PSO VMD GRNN models, the average prediction of a successful response time was faster than the average predicted response times of 127.75 ms, 91.5 ms, and 39.5 ms. The algorithm can provide the ability to predict the critical state of robot fall when the robot walked in the field, and the results can provide technical support to improve the field passability of quadruped robots for autonomous operation.