Identification Model of Soil Physical State Using the Takagi-Sugeno Fuzzy Neural Network

Adjusting tillage parameters according to soil conditions can reduce energy consumption. In this study, the working parameters and soil physical parameters of plowing were determined using a designed electric suspension platform and soil instrument. The soil conditions were classified into three physical states, namely 'hard', 'zero', and 'soft' using a fuzzy C-means clustering algorithm, taking the soil moisture content and cone penetration resistance as the grading indexes. The Takagi-Sugeno (T-S) fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs to indirectly identify the soil's physical state. The results show that when 280 groups of test data were used to verify the model, 264 groups were correctly identified, indicating a soil physical state identification accuracy of 94.29%. The T-S fuzzy neural network prediction model can achieve the real-time and accurate physical state identification of paddy soil during plowing.

Automated summary

This study determined the working parameters and soil physical parameters of plowing using a designed electric suspension platform and soil instrument. By classifying the soil conditions into three physical states and constructing a T-S fuzzy neural network classifier, the model achieved real-time and accurate identification of the soil's physical state.