Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Qu, H., Ma, W., Zhao, J., Wang, T.: Prediction method for network traffic based on maximum correntropy criterion. China Commun. 1, 134–145 (2013)
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)
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)
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)
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)
Doymaz, B., Pala, M.: The thin-layer drying characteristics of corn. J. Food Eng. 60, 125–130 (2003)
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)
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)
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)
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)
Yacinoz, T., Eminoglu, U.: Short term and medium term power distribution load forecasting by neural networks. Enegry Convers. Manag. 46, 139–314 (2005)
Movagharnejad, K., Nikzad, M.: Modeling of tomato drying using artificial neural network. Comput. Electron. Agric. 59, 78–85 (2007)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 IFIP International Federation for Information Processing
About this paper
Cite this paper
Wang, D., Li, C., Zhang, B., Tong, L. (2019). Prediction of Drying Indices for Paddy Rice in a Deep Fixed-Bed Based on Neural Network. In: Li, D. (eds) Computer and Computing Technologies in Agriculture X. CCTA 2016. IFIP Advances in Information and Communication Technology, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-030-06155-5_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-06155-5_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06154-8
Online ISBN: 978-3-030-06155-5
eBook Packages: Computer ScienceComputer Science (R0)
