Prediction of Drying Indices for Paddy Rice in a Deep Fixed-Bed Based on Neural Network

  • Danyang Wang
  • Chenghua LiEmail author
  • Benhua Zhang
  • Ling Tong
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 509)


In this study, four artificial neural network models are developed for paddy rice drying in a deep fixed-bed to predict five drying performance indices, including additional crack percentage, drying moisture uniformity, energy efficiency rate, germinating percentage and drying time. The four neural networks are BP, RBF, GRNN and ELMAN. After plenty of trials with a variety of neural network architectures, neural network with five inputs and five outputs is better than network with five inputs and any other outputs. Five drying parameters including paddy original moisture content, air temperature, air velocity, paddy thickness and tempering time are regarded as input vectors of the neural networks. The experimental results show that neural networks have good performance in predicting the paddy drying process. And also, the simulation indicate that the RBF neural network has advantages over other three neural networks in performance.


Neural network Prediction Drying indices Paddy rice Deep fixed-bed drying Drying parameters 


  1. 1.
    He, H., Bai, J., Lu, Z., Guo, Y.-F.: Electrode wear prediction in milling electrical discharge machining based on radial basis function neural network. J. Shanghai Jiaotong Univ. (Sci.) 14(6), 736–741 (2009)CrossRefGoogle Scholar
  2. 2.
    Chang, L., Pan, R., Gao, W.: Fashion color forecasting by applying an improved back propagation neural network. J. Donghua Univ. (English Edn.) 30(1), 58–62 (2013)Google Scholar
  3. 3.
    He, Y., He, H., Wang, Y., Luo, T.: Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine. J. Cent. South Univ. Technol. 18(4), 1184–1192 (2011)CrossRefGoogle Scholar
  4. 4.
    Qu, H., Ma, W., Zhao, J., Wang, T.: Prediction method for network traffic based on maximum correntropy criterion. China Commun. 1, 134–145 (2013)Google Scholar
  5. 5.
    Gao, Q., Yan, W., Shao, H.: Regularized RBF network-based inferential sensor and its application in product quality prediction. Acta Simulata Systematica Sinica 17(7), 1609–1612, 1678 (2005)Google Scholar
  6. 6.
    Zhang, Q., Yang, S.X.: Prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosyst. Eng. 83(3), 281–290 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zheng, X., Zhou, X., Xia, J.: The study on drying condition influencing paddy mill quality. J. Northeast Agric. Univ. 32(01), 48–52 (2001). (in Chinese with English abstract)Google Scholar
  8. 8.
    Wang, D.-Y., Li, C.-H.: Experiment study on influence of drying parameters on drying duration of paddy rice in a deep fixed-bed. J. Shenyang Agric. Univ. 39(2), 213–217 (2008)Google Scholar
  9. 9.
    Doymaz, B., Pala, M.: The thin-layer drying characteristics of corn. J. Food Eng. 60, 125–130 (2003)CrossRefGoogle Scholar
  10. 10.
    Ping, L., Lingke, Z., Anze, S., Xueli, J., Yanchun, L., Hui, W.: Design of forecast system of back propagation neural network based on matlab. Comput. Appl. Softw. 25(4), 149–185 (2008). (in Chinese with English abstract)Google Scholar
  11. 11.
    Zhao, C., Liu, K., Li, D.: Freight volume forecast based on GRNN. J. China Railw. Soc. 26(1), 12–15 (2004). (in Chinese with English abstract)MathSciNetGoogle Scholar
  12. 12.
    Wei, J., Ru, F.: Forecasting the traffic volume by the model of GRNN and studing it’s realization. J. Changsha Commun. Univ. 22(2), 46–50 (2006). (in Chinese with English abstract)Google Scholar
  13. 13.
    Lin, H.: Forecasting safety stock of supply chain using artificial neural network. J. Fuzhou Teach. Coll. 25(2), 72–75 (2004). (in Chinese with English abstract)Google Scholar
  14. 14.
    Yacinoz, T., Eminoglu, U.: Short term and medium term power distribution load forecasting by neural networks. Enegry Convers. Manag. 46, 139–314 (2005)CrossRefGoogle Scholar
  15. 15.
    Movagharnejad, K., Nikzad, M.: Modeling of tomato drying using artificial neural network. Comput. Electron. Agric. 59, 78–85 (2007)CrossRefGoogle Scholar
  16. 16.
    Beigi, M., Torki-Harchegani, M., Mahmoodi-Eshkaftaki, M.: Prediction of paddy drying kinetics: a comparative study between mathematical and artificial neural network modelling, 39(2016)Google Scholar
  17. 17.
    Li, J., Xiong, Q., Wang, K., et al.: A recurrent self-evolving fuzzy neural network predictive control for microwave drying process. Dry. Technol. 34(12), 1434–1444 (2016)CrossRefGoogle Scholar
  18. 18.
    Pati, J.R., Dutta, S., Eliaers, P., et al.: Experimental study of paddy drying in a vortex chamber. Dry. Technol. 34(9), 1073–1084 (2016)CrossRefGoogle Scholar
  19. 19.
    Firouzi, S., Alizadeh, M.R., Haghtalab, D.: Energy consumption and rice milling quality upon drying paddy with a newly-designed horizontal rotary dryer. Energy 119, 629–636 (2016)CrossRefGoogle Scholar
  20. 20.
    Lilhare, S.F., Bawane, N.G.: Artificial neural network based control strategies for paddy drying process. Int. J. Inf. Technol. Comput. Sci. 6(11), 28–35 (2014)Google Scholar
  21. 21.
    Mittal, G.S., Zhang, J.: Prediction of temperature and moisture content of frankfurters during thermal processing using neural network. Meat Sci. 55, 13–24 (2000)CrossRefGoogle Scholar
  22. 22.
    Fortuna, L., Giannone, P., Graziani, S., Xibilia, M.G.: Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery. IEEE Trans. Instrum. Meas. 56(1), 95–101 (2007)CrossRefGoogle Scholar
  23. 23.
    Golpour, I., Chayjan, R.A., Parian, J.A., et al.: Prediction of paddy moisture content during thin layer drying using machine vision and artificial neural networks. J. Agric. Sci. Technol. 17, 287–298 (2015)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Danyang Wang
    • 1
  • Chenghua Li
    • 2
    Email author
  • Benhua Zhang
    • 1
  • Ling Tong
    • 1
  1. 1.College of EngineeringShenyang Agricultural UniversityShenyangChina
  2. 2.College of Mechanical EngineeringShenyang Ligong UniversityShenyangChina

Personalised recommendations