DOI: https://doi.org/10.32515/2414-3820.2025.55.31-42
Influence of Parameters of Disc Coulters on the Transmission Capacity of Cutting Crop Residues in the System of Zero Tillage
About the Authors
Maksym Zayets, Associate Professor, PhD in Technical Sciences (Candidate of Technical Sciences), Associate Professor of the Department of Agroengineering and Technical Service, Polissia National University, Zhitomir, Ukraine, ORCID: https://orcid.org/0000-0002-2290-1892, e-mail: Mzaec81@gmail.com
Anatolii Klymchuk, Lecturer, Zhytomyr Automobile and Road Vocational College, National Transport University, Zhytomyr, Ukraine; ORCID: https://orcid.org/0000-0004-5090-4562, e-mail: klimchukanatoly@gmail.com
Abstract
Automated control of harvesting systems can significantly improve the efficiency of agricultural processes and reduce crop losses. Modeling and enhancing the performance of the combine harvester contribute to increasing its overall productivity. The use of machine learning methods opens up possibilities for accurate prediction of the machine’s maximum efficiency.
This study presents a combine harvester performance model developed using the Radial Basis Function (RBF) and a hybrid machine learning method–Adaptive Neuro-Fuzzy Inference System (ANFIS)–which allows predicting various combine parameters to achieve optimal performance. Additionally, the Response Surface Methodology (RSM) is applied for model optimization. Comparative analysis shows that ANFIS demonstrates better results compared to RBF.
The study of critical points enables the determination of the optimal range for each variable. The main goal of this research was to study and develop models based on RSM methods, RBF, and the ANFIS neural network, considered as key steps in constructing an accurate learning-stage model. The training process of the RBF and ANFIS models was carried out, and the obtained results were analyzed. These results allow selecting the best model for further testing. The prediction process was performed using ANFIS and RBF networks. For modeling, the parameters BS, PL, and MOG were considered dependent variables (network outputs), while A, B, and C were treated as independent variables (network inputs).
The optimization process was conducted using the Response Surface Methodology. The RSM method is a statistical approach that establishes relationships between several explanatory variables (input variables) and one or more response variables (output variables). The main idea of RSM is to use a sequence of designed experiments to achieve an optimal response.
Keywords
combine harvester, hybrid machine learning, response surface methodology, artificial intelligence, radial basis function
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References
1. Efektyvnyi obmolot zmishanykh posiviv zernovykh ta zernobobovykh kultur na zernofurazh. (2014). Kyiv: DBNU «Ahrotekh” [in Ukraine].
2. Sysolin, P.V., Rybak, T.I., & Salo, V.M. (2002). Silskohospodarski mashyny: teoretychni osnovy, konstruktsiia, proektuvannia. Knyha 2. Kiyv: Urozhai [in Ukraine].
3. Sysolin P.V., Salo V.M., Kropivnyi V.M. (2001). Silskohospodarski mashyny: teoretychni osnovy, konstruktsiia, proektuvannia. K.: Urozhai, 384 s. [in Ukraine].
4. Khailis H.A. Konovaliuk D.M. (1991). Rozrakhunok robochykh orhaniv zbyralnykh mashyn: Navch. posibnyk. K.: NMK VO, 199 s. [in Ukraine].
5. Maertens, K., M. Reyniers, and J. De Baerdemaeker, ( 2001). Design of a Dynamic Grain Flow Model for a Combine Harvester.
6. Spengler, A., S. Mehne, and A. Feiffer. Combine harvesting at large scale enterprises in Europe. in Electronic
7. Proceedings of the International Conference on Crop Harvesting and Processing, Louisville, Ky. 2003. [in English].
8. Maertens, K. and J. De Baerdemaeker, (2004). Design of a virtual combine harvester. Mathematics and Computers in Simulation, 65(1–2): p. 49–57. [in English].
9. Maertens, K. (2004). Data-driven techniques for the on-the-go evaluation of separation processes in combine harvesters., Ph. D. Thesis. Department of Agro-Engineering and Economics, Katholieke Universiteit Leuven. [in English].
10. Leuven, Belgium. Hemmat, A., V. I. Adamchuk and P. Jasa. (2008). Use of an instrumented disc coulter for mapping soil mechanical resistance. Soil & Tillage Research, 98: 150–163. [in English].
11. Fallahi, S. and M. H. Raoufat, (2008). Row-crop planter attachments in a conservation tillage system: A comparative study. Soil & Tillage Research, 98: 27–34. [in English].
12. Karayel, D. (2009). Performance of a modified precision vacuum seeder for no-till sowing of maize and soybean. Soil & Tillage Research, 104: 121–125. [in English].
13. Soyguder, S. (2011). Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system. Energy and Buildings, 43(4): p. 814–822. [in English].
14. Chen, X.-t. and L.-h. Zhang. (2014). High-quality voice conversion system based on GMM statistical parameters and RBF neural network. The Journal of China Universities of Posts and Telecommunications, 21(5): p. 68–75. [in English].
15. Craessaerts, G., et al. (2007). A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II: Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin. Biosystems Engineering, 98(3): p. 297–303. [in English].
16. Maertens, K. and J. De Baerdemaeker. (2004). Design of a virtual combine harvester. Mathematics and Computers in Simulation, 65(1): p. 49–57. [in English].
17. Zhao, Z., et al., Grain separation loss monitoring system in combine harvester. Computers and electronics in agriculture, 2011. 76(2): p. 183–188. [in English].
18. Mirzazadeh, A., et al. (2012). Intelligent modeling of material separation in combine harvester’s thresher by ANN. International Journal of Agriculture and Crop Sciences, 4(23): p. 1767–1777. [in English].
19. Maertens, K., et al., PH-Power and Machinery: An Analytical Grain Flow Model for a Combine Harvester, Part I: Design of the Model. Journal of agricultural engineering research, 2001. 79(1): p. 55–63. [in English].
20. Maertens, K., et al., PA-Precision Agriculture: An Analytical Grain Flow Model for a Combine Harvester, Part II: Analysis and Application of the Model. Journal of agricultural engineering research, 2001. 79(2): p. 187–193. [in English].
21. Miu, P.I., & Kutzbach, H.-D. (2008). Modeling and simulation of grain threshing and separation in threshing units - Part I. Computers and Electronics in Agriculture, 60(1): p. 96–104. [in English].
22. Combines. John Deere: website. URL: https://www.deere.ua/ru/zernouborochnыe-kombainы/seryia-s/s760/ [in Ukraine].
Citations
1. Ефективний обмолот змішаних посівів зернових та зернобобових культур на зернофураж. Київ : ДБНУ «Агротех”, 2014. 252 с.
2. Сисолін П.В., Рибак Т.І., Сало В.М. Сільськогосподарські машини: теоретичні основи, конструкція, проектування. Книга 2. Київ : Урожай, 2002. 364 с.
3. Сисолін П.В., Сало В.М., Кропівний В.М. Сільськогосподарські машини: теоретичні основи, конструкція, проектування. Київ : Урожай, 2001. 384 с.
4. Хайліс Г.А. Коновалюк Д.М. Розрахунок робочих органів збиральних машин: навч. посіб. Київ : НМК ВО, 1991. 199 с.
5. Maertens, K., M. Reyniers, and J. De Baerdemaeker, Design of a Dynamic Grain Flow Model for a Combine Harvester. 2001.
6. Spengler, A., S. Mehne, and A. Feiffer. Combine harvesting at large scale enterprises in Europe. in Electronic
7. Proceedings of the International Conference on Crop Harvesting and Processing, Louisville, Ky. 2003.
8. Maertens, K. and J. De Baerdemaeker, Design of a virtual combine harvester. Mathematics and Computers in Simulation, 2004. 65(1–2): p. 49–57.
9. Maertens, K., Data-driven techniques for the on-the-go evaluation of separation processes in combine harvesters. 2004, Ph. D. Thesis. Department of Agro-Engineering and Economics, Katholieke Universiteit Leuven,
10. Leuven, Belgium.Hemmat, A., V. I. Adamchuk and P. Jasa, 2008. Use of an instrumented disc coulter for mapping soil mechanical resistance. Soil & Tillage Research, 98: 150–163.
11. Fallahi, S. and M. H. Raoufat, 2008. Row-crop planter attachments in a conservation tillage system: A comparative study. Soil & Tillage Research, 98: 27–34.
12. Karayel, D., 2009. Performance of a modified precision vacuum seeder for no-till sowing of maize and soybean. Soil & Tillage Research, 104: 121–125.
13. Soyguder, S., Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system. Energy and Buildings, 2011. 43(4): p. 814–822.
14. Chen, X.-t. and L.-h. Zhang, High-quality voice conversion system based on GMM statistical parameters and RBF neural network. The Journal of China Universities of Posts and Telecommunications, 2014. 21(5): p. 68–75.
15. Craessaerts, G., et al., A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II: Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin. Biosystems Engineering, 2007. 98(3): p. 297–303.
16. Maertens, K. and J. De Baerdemaeker, Design of a virtual combine harvester. Mathematics and Computers in Simulation, 2004. 65(1): p. 49–57.
17. Zhao, Z., et al., Grain separation loss monitoring system in combine harvester. Computers and electronics in agriculture, 2011. 76(2): p. 183–188.
18. Mirzazadeh, A., et al., Intelligent modeling of material separation in combine harvester’s thresher by ANN. International Journal of Agriculture and Crop Sciences, 2012. 4(23): p. 1767–1777.
19. Maertens, K., et al., PH-Power and Machinery: An Analytical Grain Flow Model for a Combine Harvester, Part I: Design of the Model. Journal of agricultural engineering research, 2001. 79(1): p. 55–63.
20. Maertens, K., et al., PA-Precision Agriculture: An Analytical Grain Flow Model for a Combine Harvester, Part II: Analysis and Application of the Model. Journal of agricultural engineering research, 2001. 79(2): p. 187–193.
21. Miu, P.I. and H.-D. Kutzbach, Modeling and simulation of grain threshing and separation in threshing units - Part I. Computers and Electronics in Agriculture, 2008. 60(1): p. 96–104.
22. Комбайни. John Deere: веб-сайт. URL : https://www.deere.ua/ru/зерноуборочные-комбайны/серия-s/s760/ (дата звернення: 01.06.2025).
Copyright (c) 2025 Maksym Zayets, Anatoliy Klymchuk
Influence of Parameters of Disc Coulters on the Transmission Capacity of Cutting Crop Residues in the System of Zero Tillage
About the Authors
Maksym Zayets, Associate Professor, PhD in Technical Sciences (Candidate of Technical Sciences), Associate Professor of the Department of Agroengineering and Technical Service, Polissia National University, Zhitomir, Ukraine, ORCID: https://orcid.org/0000-0002-2290-1892, e-mail: Mzaec81@gmail.com
Anatolii Klymchuk, Lecturer, Zhytomyr Automobile and Road Vocational College, National Transport University, Zhytomyr, Ukraine; ORCID: https://orcid.org/0000-0004-5090-4562, e-mail: klimchukanatoly@gmail.com
Abstract
Keywords
Full Text:
PDFReferences
1. Efektyvnyi obmolot zmishanykh posiviv zernovykh ta zernobobovykh kultur na zernofurazh. (2014). Kyiv: DBNU «Ahrotekh” [in Ukraine].
2. Sysolin, P.V., Rybak, T.I., & Salo, V.M. (2002). Silskohospodarski mashyny: teoretychni osnovy, konstruktsiia, proektuvannia. Knyha 2. Kiyv: Urozhai [in Ukraine].
3. Sysolin P.V., Salo V.M., Kropivnyi V.M. (2001). Silskohospodarski mashyny: teoretychni osnovy, konstruktsiia, proektuvannia. K.: Urozhai, 384 s. [in Ukraine].
4. Khailis H.A. Konovaliuk D.M. (1991). Rozrakhunok robochykh orhaniv zbyralnykh mashyn: Navch. posibnyk. K.: NMK VO, 199 s. [in Ukraine].
5. Maertens, K., M. Reyniers, and J. De Baerdemaeker, ( 2001). Design of a Dynamic Grain Flow Model for a Combine Harvester.
6. Spengler, A., S. Mehne, and A. Feiffer. Combine harvesting at large scale enterprises in Europe. in Electronic
7. Proceedings of the International Conference on Crop Harvesting and Processing, Louisville, Ky. 2003. [in English].
8. Maertens, K. and J. De Baerdemaeker, (2004). Design of a virtual combine harvester. Mathematics and Computers in Simulation, 65(1–2): p. 49–57. [in English].
9. Maertens, K. (2004). Data-driven techniques for the on-the-go evaluation of separation processes in combine harvesters., Ph. D. Thesis. Department of Agro-Engineering and Economics, Katholieke Universiteit Leuven. [in English].
10. Leuven, Belgium. Hemmat, A., V. I. Adamchuk and P. Jasa. (2008). Use of an instrumented disc coulter for mapping soil mechanical resistance. Soil & Tillage Research, 98: 150–163. [in English].
11. Fallahi, S. and M. H. Raoufat, (2008). Row-crop planter attachments in a conservation tillage system: A comparative study. Soil & Tillage Research, 98: 27–34. [in English].
12. Karayel, D. (2009). Performance of a modified precision vacuum seeder for no-till sowing of maize and soybean. Soil & Tillage Research, 104: 121–125. [in English].
13. Soyguder, S. (2011). Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system. Energy and Buildings, 43(4): p. 814–822. [in English].
14. Chen, X.-t. and L.-h. Zhang. (2014). High-quality voice conversion system based on GMM statistical parameters and RBF neural network. The Journal of China Universities of Posts and Telecommunications, 21(5): p. 68–75. [in English].
15. Craessaerts, G., et al. (2007). A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II: Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin. Biosystems Engineering, 98(3): p. 297–303. [in English].
16. Maertens, K. and J. De Baerdemaeker. (2004). Design of a virtual combine harvester. Mathematics and Computers in Simulation, 65(1): p. 49–57. [in English].
17. Zhao, Z., et al., Grain separation loss monitoring system in combine harvester. Computers and electronics in agriculture, 2011. 76(2): p. 183–188. [in English].
18. Mirzazadeh, A., et al. (2012). Intelligent modeling of material separation in combine harvester’s thresher by ANN. International Journal of Agriculture and Crop Sciences, 4(23): p. 1767–1777. [in English].
19. Maertens, K., et al., PH-Power and Machinery: An Analytical Grain Flow Model for a Combine Harvester, Part I: Design of the Model. Journal of agricultural engineering research, 2001. 79(1): p. 55–63. [in English].
20. Maertens, K., et al., PA-Precision Agriculture: An Analytical Grain Flow Model for a Combine Harvester, Part II: Analysis and Application of the Model. Journal of agricultural engineering research, 2001. 79(2): p. 187–193. [in English].
21. Miu, P.I., & Kutzbach, H.-D. (2008). Modeling and simulation of grain threshing and separation in threshing units - Part I. Computers and Electronics in Agriculture, 60(1): p. 96–104. [in English].
22. Combines. John Deere: website. URL: https://www.deere.ua/ru/zernouborochnыe-kombainы/seryia-s/s760/ [in Ukraine].
Citations
1. Ефективний обмолот змішаних посівів зернових та зернобобових культур на зернофураж. Київ : ДБНУ «Агротех”, 2014. 252 с.
2. Сисолін П.В., Рибак Т.І., Сало В.М. Сільськогосподарські машини: теоретичні основи, конструкція, проектування. Книга 2. Київ : Урожай, 2002. 364 с.
3. Сисолін П.В., Сало В.М., Кропівний В.М. Сільськогосподарські машини: теоретичні основи, конструкція, проектування. Київ : Урожай, 2001. 384 с.
4. Хайліс Г.А. Коновалюк Д.М. Розрахунок робочих органів збиральних машин: навч. посіб. Київ : НМК ВО, 1991. 199 с.
5. Maertens, K., M. Reyniers, and J. De Baerdemaeker, Design of a Dynamic Grain Flow Model for a Combine Harvester. 2001.
6. Spengler, A., S. Mehne, and A. Feiffer. Combine harvesting at large scale enterprises in Europe. in Electronic
7. Proceedings of the International Conference on Crop Harvesting and Processing, Louisville, Ky. 2003.
8. Maertens, K. and J. De Baerdemaeker, Design of a virtual combine harvester. Mathematics and Computers in Simulation, 2004. 65(1–2): p. 49–57.
9. Maertens, K., Data-driven techniques for the on-the-go evaluation of separation processes in combine harvesters. 2004, Ph. D. Thesis. Department of Agro-Engineering and Economics, Katholieke Universiteit Leuven,
10. Leuven, Belgium.Hemmat, A., V. I. Adamchuk and P. Jasa, 2008. Use of an instrumented disc coulter for mapping soil mechanical resistance. Soil & Tillage Research, 98: 150–163.
11. Fallahi, S. and M. H. Raoufat, 2008. Row-crop planter attachments in a conservation tillage system: A comparative study. Soil & Tillage Research, 98: 27–34.
12. Karayel, D., 2009. Performance of a modified precision vacuum seeder for no-till sowing of maize and soybean. Soil & Tillage Research, 104: 121–125.
13. Soyguder, S., Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system. Energy and Buildings, 2011. 43(4): p. 814–822.
14. Chen, X.-t. and L.-h. Zhang, High-quality voice conversion system based on GMM statistical parameters and RBF neural network. The Journal of China Universities of Posts and Telecommunications, 2014. 21(5): p. 68–75.
15. Craessaerts, G., et al., A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part II: Selection of relevant input variables for identification of material other than grain (MOG) content in the grain bin. Biosystems Engineering, 2007. 98(3): p. 297–303.
16. Maertens, K. and J. De Baerdemaeker, Design of a virtual combine harvester. Mathematics and Computers in Simulation, 2004. 65(1): p. 49–57.
17. Zhao, Z., et al., Grain separation loss monitoring system in combine harvester. Computers and electronics in agriculture, 2011. 76(2): p. 183–188.
18. Mirzazadeh, A., et al., Intelligent modeling of material separation in combine harvester’s thresher by ANN. International Journal of Agriculture and Crop Sciences, 2012. 4(23): p. 1767–1777.
19. Maertens, K., et al., PH-Power and Machinery: An Analytical Grain Flow Model for a Combine Harvester, Part I: Design of the Model. Journal of agricultural engineering research, 2001. 79(1): p. 55–63.
20. Maertens, K., et al., PA-Precision Agriculture: An Analytical Grain Flow Model for a Combine Harvester, Part II: Analysis and Application of the Model. Journal of agricultural engineering research, 2001. 79(2): p. 187–193.
21. Miu, P.I. and H.-D. Kutzbach, Modeling and simulation of grain threshing and separation in threshing units - Part I. Computers and Electronics in Agriculture, 2008. 60(1): p. 96–104.
22. Комбайни. John Deere: веб-сайт. URL : https://www.deere.ua/ru/зерноуборочные-комбайны/серия-s/s760/ (дата звернення: 01.06.2025).