Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network
Jianlei Zhao | Jun Zhou | Chenyang Sun | Xu Wang | Zian Liang | Zezhong Qi
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 &lsquo:hard&rsquo:, &lsquo:zero&rsquo:, and &lsquo:soft&rsquo: using a fuzzy C-means clustering algorithm, taking the soil moisture content and cone penetration resistance as the grading indexes. The Takagi&ndash:Sugeno (T&ndash:S) fuzzy neural network classifier was constructed using traction resistance, operating velocity, and plowing depth as inputs to indirectly identify the soil&rsquo: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&ndash:S fuzzy neural network prediction model can achieve the real-time and accurate physical state identification of paddy soil during plowing.
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