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 phy...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/9/1367 |
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| author | Jianlei Zhao Jun Zhou Chenyang Sun Xu Wang Zian Liang Zezhong Qi |
| author_facet | Jianlei Zhao Jun Zhou Chenyang Sun Xu Wang Zian Liang Zezhong Qi |
| author_sort | Jianlei Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:02:36Z |
| format | Article |
| id | doaj.art-6cdb016924dd47eeac4d6aacd88db8dd |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T01:02:36Z |
| publishDate | 2022-09-01 |
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| series | Agriculture |
| spelling | doaj.art-6cdb016924dd47eeac4d6aacd88db8dd2023-11-23T14:32:48ZengMDPI AGAgriculture2077-04722022-09-01129136710.3390/agriculture12091367Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural NetworkJianlei Zhao0Jun Zhou1Chenyang Sun2Xu Wang3Zian Liang4Zezhong Qi5College of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaAdjusting 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.https://www.mdpi.com/2077-0472/12/9/1367soil moisture contentsoil compactionplowing resistancefuzzy neural network (FNN)identification |
| spellingShingle | Jianlei Zhao Jun Zhou Chenyang Sun Xu Wang Zian Liang Zezhong Qi Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network Agriculture soil moisture content soil compaction plowing resistance fuzzy neural network (FNN) identification |
| title | Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network |
| title_full | Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network |
| title_fullStr | Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network |
| title_full_unstemmed | Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network |
| title_short | Identification Model of Soil Physical State Using the Takagi–Sugeno Fuzzy Neural Network |
| title_sort | identification model of soil physical state using the takagi sugeno fuzzy neural network |
| topic | soil moisture content soil compaction plowing resistance fuzzy neural network (FNN) identification |
| url | https://www.mdpi.com/2077-0472/12/9/1367 |
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