https://doi.org/10.4081/jae.2024.1550
Comparison of two different artificial neural network models for prediction of soil penetration resistance
Published: 29 December 2023
Abstract Views: 551
PDF: 208
HTML: 5
HTML: 5
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.
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.
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.
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Alvaro Marucci, Danilo Monarca, Massimo Cecchini, Andrea Colantoni, Andrea Cappuccini, Analysis of internal shading degree to a prototype of dynamics photovoltaic greenhouse through simulation software , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Igor Kovacev, Daniele De Wrachien, Report on the 43rd International Symposium: Actual Tasks on Agricultural Engineering, 24th-27th February 2015, Opatija, Croatia , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Giacomo Costagli, Matteo Betti, Avocado oil extraction processes: method for cold-pressed high-quality edible oil production versus traditional production , Journal of Agricultural Engineering: Vol. 46 No. 3 (2015)
- Yevgeny Beiderman, Mark Kunin, Eli Kolberg, Ilan Halachmi, Binyamin Abramov, Rafael Amsalem, Zeev Zalevsky, Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle , Journal of Agricultural Engineering: Vol. 45 No. 4 (2014)
- Senanur Durgut Malçok, Azime Özkan Karabacak, Cüneyt Tunçkal, Canan Ece Tamer, Application of response surface methodology for optimisation of Cornelian cherry - Capia pepper leather dried in a heat pump drying system , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Vito Ferro, Paolo Porto, Assessing theoretical flow velocity profile and resistance in gravel bed rivers by field measurements , Journal of Agricultural Engineering: Vol. 49 No. 4 (2018)
- Alessandro Parenti, Paolo Spugnoli, Piernicola Masella, Lorenzo Guerrini, Stefano Benedettelli, Stefano Di Blasi, Comparison of grape harvesting and sorting methods on factors affecting the must quality , Journal of Agricultural Engineering: Vol. 46 No. 1 (2015)
- Yiming Xiao, Jianhua Wang, Hongyi Xiong, Fangjun Xiao, Renhuan Huang, Licong Hong, Bofei Wu, Jinfeng Zhou, Yongbin Long, Yubin Lan, Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Alessia Tonon, Lorenzo Picco, Diego Ravazzolo, Mario Aristide Lenzi, Using a terrestrial laser scanner to detect wood characteristics in gravel-bed rivers , Journal of Agricultural Engineering: Vol. 45 No. 4 (2014)
- Noureldin Sharaby, Artyom Doroshenko, Andrey Butovchenko, Modelling and verification of sesame seed particles using the discrete element method , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
<< < 22 23 24 25 26 27 28 29 30 31 > >>
You may also start an advanced similarity search for this article.
