Article Sidebar

Download
PDF
Statistic
Read Counter : 257 Download : 140

Downloads

Download data is not yet available.

Main Article Content

Abstract

The present work aims to study the development and application of Radial Basis Function (RBF) networks for predicting auger energy consumption based on input energy. The study utilized RBF networks and explored the input energy with treatments 2 (Soil moisture content), 2 (Rotary speeds), 2 (Hole depths) and 4 (Replication) based on field operations. As indicated by the results, energy input differed between the treatments but was not significant. The highest input value in transaction soil moisture content was 14.75 %, rotary speeds of 235 rpm, and hole depths of 40 cm. In comparison, the lower input energy at transaction soil moisture content was 7.9%, rotary speeds of 235 rpm, and hole depths of 20 cm. Input energy in treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9 %,235 rpm, and 20 cm) were 100.204 and 57.135 MJ. ha-1, respectively. The highest input energy shares were recorded for diesel fuel at all treatments. Furthermore, the RBF network with one hidden layer had good convergence. The output results showed 10 and five hidden neurons in a hidden layer with high accuracy for treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9%, 235 rpm, and 20 cm). In the treatment (14.75 %, 235 rpm, and 40 cm), the MSE for the training and testing sets was 0.0001 % and 0.01 % for data points with Ordinary RBF (ORBF type). The performance of the 3-10-1 architecture was better than other architectures. Finally, this research concluded that the RBF network method can forecast the input energy and energy expenditures related to the types of treatments.

Keywords

Auger Hidden Layer Human Energy Rotary Speeds Soil Moisture

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Tofeq, O. M. ., Hilal, Y. Y. ., & Hamood , H. A. . (2023). Predicting Auger Energy Consumption for Olive Orchards Using the Artificial Neural Networks. Basrah Journal of Agricultural Sciences, 36(1), 107–119. https://doi.org/10.37077/25200860.2023.36.1.09
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Al-Rajabo, S., Hilal, Y. Y., & Rajab, R. H. (2021). Investigation on the application of subsoiler vibration to reduce the energy requirement. In IOP Conference Series: Earth and Environmental Science, 653(1), 012040. IOP Publishing.
  2. https://iopscience.iop.org/article/10.1088/1755-1315/653/1/012040
  3. Al-Rubaie, S. K., & Abdulhay, H. S. (2022). Bioethanol production from olive solid residues by using Rhodotorula minuta. Iraqi Journal of Science, 63(1), 53-61.
  4. https://doi.org/10.24996/ijs.2022.63.1.6
  5. Alzoubi, I., Delavar, M. R., Mirzaei, F., & Nadjar Arrabi, B. (2020). Effect of soil properties for prediction of energy consumption in land levelling irrigation. International Journal of Ambient Energy, 41(4), 475-488.
  6. https://doi.org/10.1080/01430750.2018.1451374
  7. Balkan, B. A. (2019). System dynamics modeling of agricultural value chains: the case of olive oil in Turkey. Ph. D., Middle East Technical University. 338pp.
  8. https://hdl.handle.net/11511/43682
  9. Cappelletti, G. M., Ioppolo, G., Nicoletti, G. M., & Russo, C. (2014). Energy requirement of extra virgin olive oil production. Sustainability, 6(8), 4966-4974.
  10. https://doi.org/10.3390/su6084966
  11. El-Gendy, H. A., El-Halim, A., Morghany, H. A., & Aboukarima, A. M. (2009). Evaluating performance of a post hole digger. Journal of Soil Sciences and Agricultural Engineering, 34(5), 5783-5793.
  12. https://doi.org/10.21608/jssae.2009.93132
  13. Ghasemi-Mobtaker, H., Kaab, A., & Rafiee, S. (2020). Application of life cycle analysis to assess environmental sustainability of wheat cultivation in the west of Iran. Energy, 193, 116768.
  14. https://doi.org/10.1016/j.energy.2019.116768
  15. Hilal, Y. Y., Azmi, Y., Wan, I., & Asha'ari, Z. H. (2021). Neural Networks method in predicting oil palm FFB yields for the Peninsular States of Malaysia. Journal of Oil Palm Research, 33(3), 400-412.
  16. https://doi.org/10.21894/jopr.2020.0105
  17. Joshi, D., Eustes, A., Rostami, J., Hanson, J., & Dreyer, C. (2020). High-frequency drilling data analysis to characterize water-ice on the moon. In IADC/SPE International Drilling Conference and Exhibition. OnePetro.
  18. https://doi.org/10.2118/199684-MS
  19. Kamir, E., Waldner, F., & Hochman, Z. (2020). Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 124-135.
  20. https://doi.org/10.1016/j.isprsjprs.2019.11.008
  21. Khalilidermani, M., & Knez, D. (2022). A Survey of application of mechanical specific energy in petroleum and space drilling. Energies, 15(9), 3162.
  22. https://doi.org/10.3390/en15093162
  23. Kitani, O., & Jungbluth, T. (1999). CIGR handbook of agricultural engineering. USA: American Society of Agricultural Engineers.
  24. https://cigr.org/node/640
  25. Lo Bianco, R., Proietti, P., Regni, L., & Caruso, T. (2021). Planting systems for modern olive growing: Strengths and weaknesses. Agriculture, 11, 494.
  26. https://doi.org/10.3390/agriculture11060494
  27. Meselhy, A. A. E. (2021). Effect of variable-depth tillage system on energy requirements for tillage operation and productivity of desert soil. International Journal of Applied Agricultural Sciences, 7(1), 38.
  28. https://doi.org/10.11648/j.ijaas.20210701.13
  29. Mirjalili, S. (2019). Evolutionary Radial Basis Function Networks. Pp. 105-139. In Mirjalili, S. (Ed.). Evolutionary Algorithms and Neural Networks, Springer, Cham. 156pp.
  30. https://doi.org/10.1007/978-3-319-93025-1
  31. Ozpinar, S. (2022). Analysis of energy of different olive cultivation systems in a semiarid region. Poljoprivredna tehnika, 47(2), 58-71.
  32. https://scindeks.ceon.rs/article.aspx?artid=0554-55872202058O
  33. Pattanaik, R. K., & Mohanty, M. N. (2022). Nonlinear system identification for speech model using linear predictive coefficients based radial basis function. Journal of Information and Optimization Sciences, 43(5), 1139-1150.
  34. https://doi.org/10.1080/02522667.2022.2094551
  35. Pokhrel, A., & Soni, P. (2019). Energy balance and environmental impacts of rice and wheat production: A case study in Nepal. International Journal of Agricultural and Biological Engineering, 12(1), 201-207.
  36. https://ijabe.org/index.php/ijabe/article/view/3270
  37. Rajaeifar, M. A., Akram, A., Ghobadian, B., Rafiee, S., & Heidari, M. D. (2014). Energy-economic life cycle assessment (LCA) and greenhouse gas emissions analysis of olive oil production in Iran. Energy, 66, 139-149.
  38. https://doi.org/10.1016/j.energy.2013.12.059
  39. Rocha, H., & Dias, J. M. (2019). Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agro climatic data. Computers and Electronics in Agriculture, 157, 427-435.
  40. https://doi.org/10.1016/j.compag.2019.01.018
  41. Su, M. (2016). Research on the rapid development of the use and maintenance of earth auger. In 2016 International Conference on Economics, Social Science, Arts, Education and Management Engineering, Atlantis Press 127-131.
  42. https://doi.org/10.2991/essaeme-16.2016.25
  43. United States Agency for International Development (USAID), (2011). The future of the olive oil industry in Iraq.
  44. https://dec.usaid.gov/dec/search/FusionSearchResults.aspx?q=The+future+of+the+olive+oil+industry+in+Iraq
  45. Wu, Y., Xie, P., & Dahlak, A. (2021). Utilization of Radial Basis Function Neural Network model for water production forecasting in Seawater Greenhouse units. Energy Reports, 7, 6658-6676.
  46. https://doi.org/10.1016/j.egyr.2021.09.165