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Prediction of Combine Harvester Performance Using Hybrid Machine Learning Modeling and Response Surface Methodology

  • Tarahom Mesri Gundoshmian
  • Sina Ardabili
  • Amir MosaviEmail author
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)

Abstract

Automated controlling the harvesting systems can significantly increase the efficiency of the agricultural practices and prevent food wastes. Modeling and improvement of the combine harvester can increase the overall performance. Machine learning methods provide the opportunity of advanced modeling for accurate prediction of the highest performance of the machine. In this study, the modeling of combine harvesting id performed using radial basis function (RBF) and the hybrid machine learning method of adaptive neuro-fuzzy inference system (ANFIS) to predict various variables of the combine harvester for the optimal performance. Response surface methodology (RSM) is also used to optimize the models. The comparative study shows that the ANFIS method outperforms the RBF method.

Keywords

Combine harvester Hybrid machine learning ANFIS Response surface methodology (RSM) Artificial intelligence in agriculture Radial basis function (RBF) 

Notes

Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

References

  1. 1.
    Khorram, T.: The interaction effect of seive openess and thresher clearence on threshing quality, in Thesis of Master scienc. Department of Biosystem engineering. University of Mohaghegh Ardabili. Ardabil, Iran (2013)Google Scholar
  2. 2.
    Singh, A., Garg, I., Sharma, V.: Effect of different crop and operational parameters of a combine on grain damage during paddy harvesting. J. Res. 38(3 and 4), 241–252 (2001)Google Scholar
  3. 3.
    FAO, FAOSTAT data base. FAO, Rome (2008)Google Scholar
  4. 4.
    Craessaerts, G., et al.: A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part I: Selection of relevant input variables for identification of the sieve losses. Biosys. Eng. 98(2), 166–175 (2007)CrossRefGoogle Scholar
  5. 5.
    Maertens, K., Reyniers, M., De Baerdemaeker, J.: Design of a Dynamic Grain Flow Model for a Combine Harvester (2001)Google Scholar
  6. 6.
    Spengler, A., Mehne, S., Feiffer, A.: Combine harvesting at large scale enterprises in Europe. In: Electronic Proceedings of the International Conference on Crop Harvesting and Processing, Louisville, Ky (2003)Google Scholar
  7. 7.
    Maertens, K., De Baerdemaeker, J.: Design of a virtual combine harvester. Math. Comput. Simul. 65(1–2), 49–57 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Maertens, K.: 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, Leuven, Belgium (2004)Google Scholar
  9. 9.
    Naderloo, L., et al.: Application of ANFIS to predict crop yield based on different energy inputs. Measurement 45(6), 1406–1413 (2012)CrossRefGoogle Scholar
  10. 10.
    Gautam, R., Panigrahi, S., Franzen, D.: Neural network optimisation of remotely sensed maize leaf nitrogen with a genetic algorithm and linear programming using five performance parameters. Biosys. Eng. 95(3), 359–370 (2006)CrossRefGoogle Scholar
  11. 11.
    Soyguder, S., Alli, H.: An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with fuzzy modeling approach. Energy Build. 41(8), 814–822 (2009)CrossRefGoogle Scholar
  12. 12.
    Safa, M., Samarasinghe, S.: Determination and modelling of energy consumption in wheat production using neural networks:“A case study in Canterbury province, New Zealand”. Energy 36(8), 5140–5147 (2011)CrossRefGoogle Scholar
  13. 13.
    Mansouri raad, D.: Tractors and Agricultural Machinery, vol. 2. Publication of Bo-ali sina university. Hamadan, Iran (1993)Google Scholar
  14. 14.
    Faizollahzadeh_Ardabili, S.: Simulation and Comparison of Control System in Mushroom Growing Rooms Environment. Thesis of Master science. Department of Mechanic of Agricultural Machinery Engineering. University of Tabriz. Tabriz, Iran (2014)Google Scholar
  15. 15.
    Faizollahzadeh_Ardabili, et al.: Modeling and comparison of fuzzy and on/off controller in a mushroom growing hall. Measurement 90, 127–134 (2016)CrossRefGoogle Scholar
  16. 16.
    Jang, J.-S.R.: ANFIS: adaptive-network-based fuzzy inference system. Syst. Man Cybern. IEEE Trans. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  17. 17.
    Faizollahzadeh_Ardabili, Mahmoudi, A., Mesri Gundoshmian, T.: Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers. J. Build. Eng. 6, 301–308 (2016)CrossRefGoogle Scholar
  18. 18.
    Soyguder, S.: Intelligent system based on wavelet decomposition and neural network for predicting of fan speed for energy saving in HVAC system. Energy Build. 43(4), 814–822 (2011)CrossRefGoogle Scholar
  19. 19.
    Chen, X.-T., Zhang, L.-H.: High-quality voice conversion system based on GMM statistical parameters and RBF neural network. J. China Universities Posts Telecommun. 21(5), 68–75 (2014)CrossRefGoogle Scholar
  20. 20.
    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. Biosys. Eng. 98(3), 297–303 (2007)CrossRefGoogle Scholar
  21. 21.
    Maertens, K., De Baerdemaeker, J.: Design of a virtual combine harvester. Math. Comput. Simul. 65(1), 49–57 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Zhao, Z., et al.: Grain separation loss monitoring system in combine harvester. Comput. Electron. Agric. 76(2), 183–188 (2011)CrossRefGoogle Scholar
  23. 23.
    Mirzazadeh, A., et al.: Intelligent modeling of material separation in combine harvester’s thresher by ANN. Int. J. Agric. Crop Sci. 4(23), 1767–1777 (2012)Google Scholar
  24. 24.
    Maertens, K., et al.: PH—power and machinery: an analytical grain flow model for a combine harvester, Part I: design of the model. J. Agric. Eng. Res. 79(1), 55–63 (2001)CrossRefGoogle Scholar
  25. 25.
    Maertens, K., et al.: PA—precision agriculture: an analytical grain flow model for a combine harvester, part ii: analysis and application of the model. J. Agric. Eng. Res. 79(2), 187–193 (2001)CrossRefGoogle Scholar
  26. 26.
    Miu, P.I., Kutzbach, H.-D.: Modeling and simulation of grain threshing and separation in threshing units—Part I. Comput. Electron. Agric. 60(1), 96–104 (2008)CrossRefGoogle Scholar
  27. 27.
    Miu, P.I., Kutzbach, H.-D.: Modeling and simulation of grain threshing and separation in axial threshing units: Part II. Application to tangential feeding. Comput. Electron. Agric. 60(1), 105–109 (2008)CrossRefGoogle Scholar
  28. 28.
    Peter, I.M.: Optimal Design Threshing Units Based on a Genetic Algorithm. I. Algorithm. ASAEGoogle Scholar
  29. 29.
    Miu, P.I., Kutzbach, H.D.: Simulation of threshing and separation processes in threshing units. Agrartechnische Forschung 6, 1–7 (2000)Google Scholar
  30. 30.
    Myhan, R., Jachimczyk, E.: Grain separation in a straw walker unit of a combine harvester: Process model. Biosys. Eng. 145, 93–107 (2016)CrossRefGoogle Scholar
  31. 31.
    Mosavi, A., Edalatifar, M.: A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration, in Lecture Notes in Networks and Systems, pp. 235–243. Springer (2019)Google Scholar
  32. 32.
    Mosavi, A., Lopez, A., Várkonyi-Kóczy, A.R.: Industrial applications of big data: state of the art survey, D. Luca, L. Sirghi, and C. Costin, Editors, pp. 225–232. Springer (2018)Google Scholar
  33. 33.
    Mosavi, A., Ozturk, P., Chau, K.W.: Flood prediction using machine learning models: literature review. Water (Switzerland) 10(11) (2018)CrossRefGoogle Scholar
  34. 34.
    Mosavi, A., Rabczuk, T.: Learning and intelligent optimization for material design innovation. In: Kvasov, D.E. et al. (eds.), pp. 358–363. Springer (2017)Google Scholar
  35. 35.
    Mosavi, A., Rabczuk, T., Várkonyi-Kóczy, A.R.: Reviewing the novel machine learning tools for materials design. In: Luca, D., Sirghi, L., Costin, C. (eds.), pp. 50–58. Springer (2018)Google Scholar
  36. 36.
    Mosavi, A., et al.: State of the art of machine learning models in energy systems, a systematic review. Energies 12(7) (2019)CrossRefGoogle Scholar
  37. 37.
    Mosavi, A., et al.: Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Eng. Appl. Comput. Fluid Mech. 13(1), 482–492 (2019)Google Scholar
  38. 38.
    Mosavi, A., Várkonyi-Kóczy, A.R.: Integration of machine learning and optimization for robot learning. In: Jablonski, R., Szewczyk, R. (eds.), pp. 349–355. Springer (2017)Google Scholar
  39. 39.
    Nosratabadi, S., et al.: Sustainable business models: a review. Sustainability (Switzerland) 11(6) (2019)Google Scholar
  40. 40.
    Qasem, S.N., et al.: Estimating daily dew point temperature using machine learning algorithms. Water (Switzerland) 11(3) (2019)CrossRefGoogle Scholar
  41. 41.
    Rezakazemi, M., Mosavi, A., Shirazian, S.: ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 274, 470–476 (2019)CrossRefGoogle Scholar
  42. 42.
    Riahi-Madvar, H., et al.: Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Eng. Appl. Comput. Fluid Mech. 13(1), 529–550 (2019)Google Scholar
  43. 43.
    Shabani, S., Samadianfard, S., Taghi Sattari, M., Shamshirband, S., Mosavi, A., Kmet, T., Várkonyi-Kóczy, A.R.: Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression. Preprints (2019), 2019070351.  https://doi.org/10.20944/preprints201907.0351.v1
  44. 44.
    Shamshirband, S., Hadipoor, M., Baghban, A., Mosavi, A., Bukor J., Várkonyi-Kóczy, A.R.: Developing an ANFIS-PSO Model to predict mercury emissions in Combustion Flue Gases. Preprints (2019), 2019070165.  https://doi.org/10.20944/preprints201907.0165.v1
  45. 45.
    Shamshirband, S., et al.: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng. Appl. Comput. Fluid Mech. 13(1), 91–101 (2019)Google Scholar
  46. 46.
    Shamshirband, S., Mosavi, A., Rabczuk, T.: Particle swarm optimization model to predict scour depth around bridge pier. arXiv preprint arXiv:1906.08863 (2019)
  47. 47.
    Taherei Ghazvinei, P., et al.: Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng. Appl. Comput. Fluid Mech. 12(1), 738–749 (2018)Google Scholar
  48. 48.
    Torabi, M., et al.: A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy 38(1), 66–76 (2019)CrossRefGoogle Scholar
  49. 49.
    Torabi, M., et al.: A hybrid machine learning approach for daily prediction of solar radiation. In: Lecture Notes in Networks and Systems, pp. 266–274. Springer (2019)Google Scholar
  50. 50.
    Aram, F., et al.: Design and validation of a computational program for analysing mental maps: aram mental map analyzer. Sustainability (Switzerland). 11(14) (2019)CrossRefGoogle Scholar
  51. 51.
    Asadi, E., et al.: Groundwater Quality Assessment for Drinking and Agricultural Purposes in Tabriz Aquifer, Iran (2019)Google Scholar
  52. 52.
    Asghar, M.Z., Subhan, F., Imran, M., Kundi, F.M., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content. Pre-prints 2019, 2019080019.  https://doi.org/10.20944/preprints201908.0019.v1
  53. 53.
    Bemani, A., Baghban, A., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Sol-vent Solubility in Supercritical CO2. Preprints (2019), 2019060055.  https://doi.org/10.20944/preprints201906.0055.v2
  54. 54.
    Choubin, B., et al.: Snow avalanche hazard prediction using machine learning methods. J. Hydrol., 577 (2019)CrossRefGoogle Scholar
  55. 55.
    Choubin, B., et al.: An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096 (2019)CrossRefGoogle Scholar
  56. 56.
    Dehghani, M., et al.: Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(2) (2019)CrossRefGoogle Scholar
  57. 57.
    Dineva, A., et al.: Review of soft computing models in design and control of rotating electrical machines. Energies 12(6) (2019)CrossRefGoogle Scholar
  58. 58.
    Dineva, A., et al.: Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines (2019)Google Scholar
  59. 59.
    Farzaneh-Gord, M., et al.: Numerical simulation of pressure pulsation effects of a snubber in a CNG station for increasing measurement accuracy. Eng. Appl. Comput. Fluid Mech. 13(1), 642–663 (2019)Google Scholar
  60. 60.
    Ghalandari, M., et al.: Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis. Eng. Appl. Comput. Fluid Mech. 13(1), 519–528 (2019)Google Scholar
  61. 61.
    Ghalandari, M., et al.: Flutter speed estimation using presented differential quadrature method formulation. Eng. Appl. Comput. Fluid Mech. 13(1), 804–810 (2019)Google Scholar
  62. 62.
    Karballaeezadeh, N., et al.: Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Eng. Appl. Comput. Fluid Mech. 13(1), 188–198 (2019)Google Scholar
  63. 63.
    Menad, N.A., et al.: Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Eng. Appl. Comput. Fluid Mech. 13(1), 724–743 (2019)MathSciNetGoogle Scholar
  64. 64.
    Mohammadzadeh, S., et al.: Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures 4(2), 26 (2019)CrossRefGoogle Scholar
  65. 65.
    Wen, X.-L., Wang, H.-T., Wang, H.: Prediction model of flow boiling heat transfer for R407C inside horizontal smooth tubes based on RBF neural network. Procedia Eng. 31, 233–239 (2012)CrossRefGoogle Scholar
  66. 66.
    Jiang, H., et al.: Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Convers. Manag. 95, 42–58 (2015)CrossRefGoogle Scholar
  67. 67.
    Riverol, C., Di Sanctis, C.: Improving adaptive-network-based fuzzy inference systems (ANFIS): a practical approach. Asian J. Inf. Technol. 4(12), 1208–1212 (2005)Google Scholar
  68. 68.
    Chaabene, M., Ammar, M.B.: Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems. Renew. Energy 33(7), 1435–1443 (2008)CrossRefGoogle Scholar
  69. 69.
    Ardabili, S.F., et al.: A novel enhanced exergy method in analysing HVAC system using soft computing approaches: a case study on mushroom growing hall. J. Build. Eng. (2017)Google Scholar
  70. 70.
    Ardabili, S., Mosavi, A., Mahmoudi, Mesri Gundoshmian, T., Nosratabadi, S., Varkonyi-Koczy, A.: Modelling temperature variation of mushroom growing hall using artificial neural networks, Preprints (2019)Google Scholar
  71. 71.
    Mesri Gundoshmian, T., Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Prediction of combine harvester performance using hybrid machine learning modeling and re-sponse surface methodology, Preprints (2019)Google Scholar
  72. 72.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A., Systematic review of deep learning and machine learning models in biofuels research, Preprints (2019)Google Scholar
  73. 73.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Advances in machine learning model-ing reviewing hybrid and ensemble methods, Preprints (2019)Google Scholar
  74. 74.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Building Energy information: demand and consumption prediction with Machine Learning models for sustainable and smart cities, Preprints (2019)Google Scholar
  75. 75.
    Ardabili, S., Mosavi, A., Dehghani, M., Varkonyi-Koczy, A.: Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review, Preprints (2019)Google Scholar
  76. 76.
    Mohammadzadeh, D., Karballaeezadeh, N., Mohemmi, M., Mosavi, A., Varkonyi-Koczy, A.: Urban Train Soil-Structure Interaction Modeling and Analysis, Preprints (2019)Google Scholar
  77. 77.
    Mosavi, A., Ardabili, S., Varkonyi-Koczy, A., List of deep learning models, Preprints (2019)Google Scholar
  78. 78.
    Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., Aram, F.: State of the art survey of deep learning and machine learning models for smart cities and urban sustainability, Preprints (2019)Google Scholar

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Authors and Affiliations

  1. 1.Department of Biosystem EngineeringUniversity of Mohaghegh ArdabiliArdabilIran
  2. 2.Institute of Advanced Studies KoszegKoszegHungary
  3. 3.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  4. 4.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  5. 5.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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