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

Jianlei Zhao, Jun Zhou (), Chenyang Sun, Xu Wang, Zian Liang and Zezhong Qi
Additional contact information
Jianlei Zhao: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jun Zhou: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Chenyang Sun: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xu Wang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Zian Liang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Zezhong Qi: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2022, vol. 12, issue 9, 1-17

Abstract: 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.

Keywords: soil moisture content; soil compaction; plowing resistance; fuzzy neural network (FNN); identification (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/9/1367/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/9/1367/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:9:p:1367-:d:904648

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().