Comparison of two different artificial neural network models for prediction of soil penetration resistance

Published: 29 December 2023
Abstract Views: 314
PDF: 30
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

A time-varying, nonlinear soil-plant system contains many unknown elements that can be quantified based on analytical methodologies. Artificial neural networks (ANNs) are a widely used mathematical computing, modeling, and predicting methods that estimate unknown values of variables from known values of others. This paper aims to simulate the relationship between soil moisture, bulk density, porosity ratio, depth, and penetration resistance and to estimate soil penetration resistance with the help of ANNs. For this aim, the generalized regression neural network (GRNN) and radial basis function (RBF) models were developed and compared for the estimation of soil penetration resistance values in MATLAB. A dataset of 153 samples was collected from experimental field. From the 153 data, 102 data (33%) were selected for training and the remaining 51 data (67%) were used for testing. The estimation process implemented 10 replications using randomly selected testing and training data. mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate estimation accuracy on the developed ANN methods. Based on MSE, RMSE, MAE and standard deviation, statistical results showed that the GRNN modeling presented better results than the RBF model in predicting soil penetration resistance success.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Abrougui K., Chehaibi S., Louvet J.N., Hannachi C. Destain M.F. 2012. Soil structure and the effect of tillage systems. Bull. Uni. Agric. Sci. Vet. Med. Cluj-Napoca Agric. 69:11-6. DOI: https://doi.org/10.15835/buasvmcn-agr:8694
Abrougui K., Gabsi K., Elaoud A., Fki H., Chenini I. Chehaibi S. 2014. Modular feed forward networks to predict soil penetration resistance from tillage technique and working depth. Int. J. Curr. Eng. Technol. 4:3567-73. DOI: https://doi.org/10.14741/Ijcet/22774106/4.6.2014.86
Bayat H., Ebrahim Zadeh G. 2018. Estimation of the soil water retention curve using penetration resistance curve models. Comput. Electron. Agric. 144:329-43. DOI: https://doi.org/10.1016/j.compag.2017.10.015
Bennedsen B.S., Peterson D.L., Tabb A. 2007. Identifying apple surface defects using principal components analysis and artificial neural networks. Trans. ASAE. 50:2257-65. DOI: https://doi.org/10.13031/2013.24078
Borges P.H.M., Mendoza Z.M.S.H., Maia J.C.S., Bianchini A., Fernandes H.C. 2017. Estimation of fuel consumption in agrıcultural mechanized operations using artificial neural networks. Eng. Agríc. 37:136-47. DOI: https://doi.org/10.1590/1809-4430-eng.agric.v37n1p136-147/2017
Chia K.S., Abdul Rahim H. Abdul Rahim R. 2012. Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network. Biosyst. Eng. 113:158-65. DOI: https://doi.org/10.1016/j.biosystemseng.2012.07.003
Colombi T., Keller T. 2019. Developing strategies to recover crop productivity after soil compaction – A plant eco-physiological perspective. Soil Tillage Res. 191:156-61. DOI: https://doi.org/10.1016/j.still.2019.04.008
Elnesr M.N. Alazba A.A. 2017. Simulation of water distribution under surface dripper using artificial neural networks. Comput. Electron. Agric. 143:90-9. DOI: https://doi.org/10.1016/j.compag.2017.10.003
Faris H., Alkasassbeh M., Rodan A. 2014. Artificial neural networks for surface ozone prediction: Models and analysis. Polish J. Environ. Stud. 23:341-8.
Hosseini M., Movahedi N., Seyed A.R., Dehghani A.A., Khaledian Y. 2016. Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods. Soil Tillage Res. 157: 32-42. DOI: https://doi.org/10.1016/j.still.2015.11.004
Hosseini M., Movahedi N. Seyed A.R., Dehghani A.A., Zeraatpisheh M. 2018. Modeling of soil mechanical resistance using intelligent methods. J. Soil Sci. Plant Nutr. 18:939-51. DOI: https://doi.org/10.4067/S0718-95162018005002702
Jiang Q., Cao M., Wang Y., Wang J. 2020. Estimating Soil Penetration Resistance of Paddy Soils in the Plastic State Using Physical Properties. Agronomy 10:1914. DOI: https://doi.org/10.3390/agronomy10121914
Kandirmaz H.M., Kaba K., Avci M. 2014. Estimation of monthly sun shine duration in Turkey using artificial neural networks. Int. J. Photoenergy 2014:1-9. DOI: https://doi.org/10.1155/2014/680596
Kurup P., Griffin E. 2006. Prediction of soil composition from CPT data using general regression neural network. J. Comput. Civ. Eng. 20:281-9. DOI: https://doi.org/10.1061/(ASCE)0887-3801(2006)20:4(281)
Leung M.T., Chen A., Daouk H. 2000. Forecasting exchange rates using general regression neural networks. Comput. Operations Res. 27:1093-110. DOI: https://doi.org/10.1016/S0305-0548(99)00144-6
Lima R.P., Silva A.P., Giarola N.F.B., Silva A.R., Rolim M.M. 2017. Changes in soil compaction indicators in response to agricultural field traffic. Biosyst. Eng. 162:110. DOI: https://doi.org/10.1016/j.biosystemseng.2017.07.002
Lines S., Williams D.J., Galindo-Torres S.A. 2017. Determination of thermal conductivity of soil using standard cone penetration test. Energy Procedia. 118:172-8. DOI: https://doi.org/10.1016/j.egypro.2017.07.036
Manonmani A., Thyagarajan T., Elango M., Sutha S. 2018. Modeling and control of greenhouse system using neural networks. Trans. Inst. Meas. Control 40:918-29. DOI: https://doi.org/10.1177/0142331216670235
Mohammadi Torkashvand A., Ahmadi A., Gómez P.A. Maghoumi M. 2019. Using artificial neural network in determining postharvest life of kiwifruit. J. Sci. Food Agric. 99:5918-25. DOI: https://doi.org/10.1002/jsfa.9866
Niedbala G. 2019. Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield. J. Integr. Agric. 18:54-61. DOI: https://doi.org/10.1016/S2095-3119(18)62110-0
Palani S., Liong S.Y., Tkalich P. 2008. An ANN application for water quality forecasting. Mar. Pollut. Bull. 56:1586-97. DOI: https://doi.org/10.1016/j.marpolbul.2008.05.021
Pereira T.D.S., Robaina A.D., Peiter M.X., Torres R.R., Bruning J. 2018. The use of artificial intelligence for estimating soil resistance to penetration. Eng. Agríc. 38:142-8. DOI: https://doi.org/10.1590/1809-4430-eng.agric.v38n1p142-148/2018
Reyes J., Thiers O., Gerding V. 2014. Characterization of soil properties of Nothofagus spp. forest with and without scarification in the Andean region of Southern Chile. J. Soil Sci. Plant Nutr. 14:101-13. DOI: https://doi.org/10.4067/S0718-95162014005000008
Rizaldi T., Hermawan W., Mandang T., Pertiwi S., Rudiyanto. 2018. Development of the method on the prediction of soil plat penetration resistance. Sci. Agric. Bohem. 49:325-32. DOI: https://doi.org/10.2478/sab-2018-0039
Santos F.L., Mendes De Jesus V.A., Valente D.S.M. 2012. Modeling of soil penetration resistance using statistical analyses and artificial neural networks. Acta Scientiarum Agron. 34:219-24. DOI: https://doi.org/10.4025/actasciagron.v34i2.11627
Sathiesh Kumar V., Gogul I., Deepan Raj M., Pragadesh S.K., Sarathkumar Sebastin J. 2016. Smart autonomous gardening rover with plant recognition using neural networks. Procedia Comput. Sci. 93:975-81. DOI: https://doi.org/10.1016/j.procs.2016.07.289
Silva W.M., Bianchini A., Cunha C.A. 2016. Modeling and correction of soil penetration resistance for variations in soil moisture and soil bulk density. Eng. Agríc. 36:449-59. DOI: https://doi.org/10.1590/1809-4430-Eng.Agric.v36n3p449-459/2016
Silveira C.T., Oka-Fiori C., Santos L.J.S., Sirtoli A.E.A., Silva C.R. Botelho M.F. 2013. Soil prediction using artificial neural networks and topographic attributes. Geoderma 195-196:165-72. DOI: https://doi.org/10.1016/j.geoderma.2012.11.016
Siqueira G.M., Dafonte J.D., Lema J.B., Armesto M.V., Silva E.F. 2014. Using Soil Apparent Electrical Conductivity to Optimize Sampling of Soil Penetration Resistance and to Improve the Estimations of Spatial Patterns of Soil Compaction. Sci. World J. 2014:1-12. DOI: https://doi.org/10.1155/2014/269480
Specht D.F. 1991. A general regression neural network. IEEE Trans. Neural Netw. 2:568-76. DOI: https://doi.org/10.1109/72.97934
Yuguo Z., Tong M., Zhong-Hua P., Changbing Z. 2019. A survey on hyper basis function neural networks. Syst. Sci. Control Eng. 7:495-507. DOI: https://doi.org/10.1080/21642583.2019.1699474
Zhang H., Song T.Q., Wang K.L., Wang G.X., Hu H., Zeng F.P. 2012. Prediction of crude protein content in rice grain with canopy spectral reflectance. Plant Soil Environ. 58:514-20. DOI: https://doi.org/10.17221/526/2012-PSE
Zhou R., Li Y. 2007. Texture analysis of MR image for predicting the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network. Magn. Reson. Imag. 25:727-32. DOI: https://doi.org/10.1016/j.mri.2006.09.011

How to Cite

Ünal, İlker, Kabaş, Önder and Sözer, S. (2023) “Comparison of two different artificial neural network models for prediction of soil penetration resistance”, Journal of Agricultural Engineering, 55(1). doi: 10.4081/jae.2024.1550.