Chisel plow characteristics impact on power-fuel consumption, stubble cover, and surface roughness using deep learning neural networks with sensitivity analysis

Altikat S., ÇELİK A., BOYDAŞ M. G., Malasli M. Z., Altikat A.

Scientific Reports, cilt.14, sa.1, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1038/s41598-024-80253-0
  • Dergi Adı: Scientific Reports
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: Chisel, Conservation tillage, Deep learning neural network, Draft power, Sensitivity analysis, Soil conservation
  • Atatürk Üniversitesi Adresli: Evet

Özet

The aim of this study was to determine the effects of different shank types, tine types, and tractor forward speeds on the power and fuel consumption of chisel plows, stubble cover, soil surface roughness, and penetration resistance. In addition, using the obtained results, the draft power, fuel consumption, soil surface roughness, and soil stubble cover rate were modeled using Deep Learning Neural Network (DLNN) architectures and sensitivity analysis of these models were performed. Four different shank types (rigid, semi-spring, spring, and vibrating) and two different tine types (conventional and winged) were used at three different tractor forward speeds (4.5, 5.4 and 6.3 km.h− 1) were tested to this end. The obtained results indicated that the maximum draft power was achieved with the rigid type shank. The highest soil surface roughness values were observed for the vibrating shank type and winged tine type. Sixteen different network architectures were conducted using deep learning neural network methods and draft power, fuel consumption, soil surface roughness, and percent stubble cover were modeled. Sensitivity analyses were performed to indicate which modeled parameters were more sensitive to the factors using the obtained models. Draft power was modeled with 97.7% accuracy using the DLNN9 network architecture. Additionally, fuel consumption and soil roughness were best modeled with the DLNN13 network architecture, R2 values for those targets were 0.929 and 0.930 respectively. According to the sensitivity analysis, draft power, fuel consumption, soil roughness, and stubble cover rate were significantly affected by changes in the physical properties of the soil.