Application of Artificial Neural Networks in Soil Science Research

Elakiya, N *

Department of Soil Science, J.K.K. Munirajah College of Agricultural Science, (Affiliated to TNAU), Gobichettipalayam, Erode, India.

G. Keerthana

Kerala Agricultural University, Kerala, India.

*Author to whom correspondence should be addressed.


Artificial Neural Networks utilize high-performance computation and large data technology, allowing research to generate new prospects in agriculture. ANN is currently a preferred technique for crop yield prediction, forecasting, and classification in biological science domains. Among different agriculture fields, soil science research plays a vital role in understanding and managing the complex processes occurring within the soil environment as oil is a complex system with dynamic surface layers that differ from the other parts of the matrix. Due to the increasing accessibility of innovative computing techniques, Artificial Neural Networks (ANNs) have developed into useful tools for modeling and forecasting soil-related activities. The numerous applications of ANNs in soil science research, with a focus on how well they can classify soils, assess soil fertility, forecast soil erosion, and estimate soil moisture. They are vital tools for identifying soil types, evaluating fertility levels, predicting erosion, and soil moisture estimation. ANN models were effective at predicting soil characteristics like pH, organic carbon concentration, and clay content. By training on vast datasets that contain the chemical, biological, and physical properties of soil, ANNs are able to accurately predict different soil types and enable land-use planning, precision farming, and environmental management. This mini-review focuses on ANN approaches that possess the potential to increase our understanding of soil science and encourage informed decisions for soil management and conservation.

Keywords: ANN, input-output, prediction, accuracy, soil properties, precision agriculture

How to Cite

Elakiya, N, & Keerthana, G. (2024). Application of Artificial Neural Networks in Soil Science Research. Archives of Current Research International, 24(5), 1–15.


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Goutham DR, Krishnaiah AJ. Application of artificial neural networking technique to predict the geotechnical aspects of expansive soil: A review. International Journal Engineering and Manufacturing. 2021;11(6):48-53. DOI:10.5815/ijem.2021.06.0

Siti Khairunniza-Bejo, Samihah Mustaffha, Wan Ishak Wan Ismail. Application of artificial neural network in predicting crop yield: A review. Journal of Food Science and Engineering. 2014;4:1-9.

Kujawa S, Niedbała G. Artificial neural networks in agriculture. Agriculture. 2021; 11(6):497.

Lai J, Qiu J, Feng Z, Chen J, Fan H. Prediction of soil deformation in tunnelling using artificial neural networks. Computational Intelligence and Neuroscience. 2016;2016:33. Available:

Jain AK, Mao J, Mohiuddin KM. Artificial neural networks: A tutorial. Computer. 1996;29(3):31-44.

Boniecki P, Koszela K, Świerczyński K, Skwarcz J, Zaborowicz M, Przybył J. Neural visual detection of grain weevil (Sitophilus granarius L.). Agriculture. 2020; 10(1):25.

Niedbała G, Kurasiak-Popowska D, Stuper-Szablewska K, Nawracała J. Application of artificial neural networks to analyze the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat grain. Agriculture. 2020;10(4): 127.

Li Y, Chao X. ANN-based continual classification in agriculture. Agriculture. 2020;10(5):178.

Aji GK, Hatou K, Morimoto T. Modeling the dynamic response of plant growth to root zone temperature in hydroponic chili pepper plant using neural networks. Agriculture. 2020;10(6):234.

García-Martínez H, Flores-Magdaleno H, Ascencio-Hernández R, Khalil-Gardezi A, Tijerina-Chávez L, Mancilla-Villa OR, Vázquez-Peña MA. Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture. 2020;10(7):277.

Dahikar SS and Rode SV. Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering. 2014;2(1).

Shahin MA. State-of-the-art review of some artificial intelligence applications in pile foundations. Geoscience Frontiers. 2016;7(1):33-44.

Sarmadian F, Taghizadeh Mehrjardi R. Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan Province, North of Iran. Global Journal of Environmental Research. 2008;2(1):30-5.

Dhawale AW, Banne SP. Comparative study of application of artificial neural networks for predicting engineering properties of soil: A review. In Proceedings of Fourth International Conference on Inventive Material Science Applications. ICIMA. 2021:751-763.

Pentoś K, Pieczarka K, Serwata K. The relationship between soil electrical parameters and compaction of sandy clay loam soil. Agriculture. 2021;11(2):114.

Ekhmaj AI. Predicting soil infiltration rate using artificial neural network. In 2010 International Conference on Environmental Engineering and Applications. IEEE. 2010; 117-121.

Gutiérrez PA, López-Granados F, Peña-Barragán JM, Jurado-Expósito M, Hervás-Martínez C. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Computers and Electronics in Agriculture. 2008;64(2):293-306.

Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture. 2010;71(2):107-27.

Kravchenko AN, Bullock DG. Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal. 2000;92(1):75-83.

Park SJ, Hwang CS, Vlek PL. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agricultural Systems. 2005;85(1):59-81.

Das P. Study on Machine Learning Techniques Based Hybrid Model for Forecasting in Agriculture. Ph.D. Thesis, PG-school IARI, New Delhi, India; 2019.

Abdipour M, Younessi-Hmazekhanlu M, Ramazani SH. Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.). Industrial Crops and Products. 2019; 127:185-94.

Guimarães BVC, Donato SLR, Aspiazú I, Azevedo AM. Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural. Pesq. Agropec. Trop. Goiânia. 2021;51:1–11.

Schultz A, Wieland R, Lutze G. Neural networks in agroecological modeling-stylish application or helpful tool? Comput. Electron. Agric. 2000;29:73–97.

Fortin JG, Anctil F, Parent LÉ, Bolinder MA. A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada. Comput. Electron. Agric. 2010;73:126–132.

Mansouri A, Fadavi A, Mortazavian SMM. An artificial intelligence approach for modeling volume and fresh weight of callus–A case study of cumin (Cuminum cyminum L.). J. Theor. Biol. 2016;397: 199–205.

Treder W. Relationship between yield, crop density coefficient and average fruit weight of ‘gala’ apple. J. Fruit Ornam. Plant Res. 2008;16:53–63.

Gholipoor M, Rohani A, Torani S. Optimization of traits to increasing barley grain yield using an artificial neural network. Int. J. Plant Prod. 2013;7: 1–17.

Arel E. Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data. Computers & Geosciences. 2012;43: 90-100.

Farrokhzad F, Choobbasti AJ, Barari A. Liquefaction microzonation of Babol city using artificial neural network. Journal of King Saud University-Science. 2012;24(1): 89-100.

Ellis GW, Yao C, Zhao R, Penumadu D. Stress-strain modeling of sands using artificial neural networks. Journal of Geotechnical Engineering. 1995;121(5): 429-35.

Turk G, Logar J, Majes B. Modelling soil behaviour in uniaxial strain conditions by neural networks. Advances in Engineering Software. 2001;32(10-11):805-12.

Tizpa P, Chenari RJ, Fard MK, Machado SL. ANN prediction of some geotechnical properties of soil from their index parameters. Arab J Geosci. 2015;8: 2911–2920.

Günaydın OJ. Estimation of soil compaction parameters by using statistical analyses and artificial neural networks. Environmental Geology. 2009;57:203-15.

Shahiri J, Ghasemi M. Utilization of soil stabilization with cement and copper slag as subgrade materials in road embankment construction. International Journal of Transportation Engineering. 2017;5(1):45-58.

Alavi AH, Gandomi AH, Mollahassani A, Heshmati AA, Rashed A. Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci. 2010;173(3):368–379.

Das SK, Samui P, Sabat AK. Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil. Geotechnical and Geological Engineering. 2011;29:329-342.

Das SK, Suman S. Prediction of lateral load capacity of pile in clay using multivariate adaptive regression spline and functional network. Arab J Sci Eng. 2015; 40(6):1565–1578.

DOI: 10.1007/s13369-015-1624-y

Suman S, Mahamaya M, Das SK. Prediction of maximum dry density and unconfined compressive strength of cement stabilized soil using artificial intelligence techniques. International Journal of Geosynthetics and Ground Engineering. 2016;2:1-11.

Sinha SK, Wang MC. Artificial neural network prediction models for soil compaction and permeability. Geotechnical and Geological Engineering. 2008;26: 47-64.

Grzegorz Wrzesiński, Markiewicz A. Prediction of permeability coefficient k in sandy soils using ANN. Sustainability. 2022;14(11):6736. Available:

Erzin Y, Gumaste SD, Gupta AK, Singh DN. Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils. Canadian Geotechnical Journal. 2009;46(8):955-68.

Armaghani DJ, Mirzaei F, Shariati M, Trung NT, Shariati M, Trnavac D. Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech. Eng. 2020;20(3):191-205.

Mousavi SM, Alavi AH, Gandomi AH, Mollahasani A. Nonlinear genetic-based simulation of soil shear strength parameters. Journal of Earth System Science. 2011;120:1001-1022.

Kanung D, Sharma S, Pain A. Artificial Neural Network (ANN) and regression tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters. Frontiers of Earth Science, 2014;8(3):439-456.

Dutta RK, Singh A, Gnananandarao T. Prediction of free swell index for the expansive soil using artificial neural networks. Journal of Soft Computing in Civil Engineering. 2019;3(1):47-62.

Shahin MA, Maier HR, Jaksa MB. Data division for developing neural networks applied to geotechnical engineering. Journal of Computing in Civil Engineering. 2004;18(2):105–114.

Narendra BS, Sivapullaiah PV, Suresh S, Omkar SN. Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study. Computers and Geotechnics. 2006;33(3):196–208.

Kalkan E, Akbulut S, Tortum A, Celik A. Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environmental Geology (Berlin). 2009;58(7):1429–1440.

Sathyapriya S, Arumairaj P, Ranjini D. Prediction of unconfined compressive strength of a stabilized expansive clay soil using ANN and regression analysis (SPSS). Asian Journal of Research in Social Sciences and Humanities. 2017; 7(2):109-123.

Tabarsa A, Latifi N, Osouli A, Bagheri Y. Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support vector machines. Frontiers of Structural and Civil Engineering. 2021;15(2):520-36.

Wang O, Al-Tabbaa A. Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks. In: ASCE International Workshop on Computing in Civil Engineering. Los Angeles, CA. 2013; 299–306.

Merouane FZ, Mamoune SM. Prediction of swelling parameters of two clayey soils from Algeria using artificial neural networks. Modelling in Civil Environmental Engineering. 2018;14(3):11-26.

Ikizler SB, Aytekin M, Vekli M, Kocabaş F. Prediction of swelling pressures of expansive soils using artificial neural networks. Advances in Engineering Software. 2010;41(4):647-655.

Minasny B, Mc Bratney AB. A Conditioned latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences. 2006; 32: 1378-1388 Available:

Daniel KW, Tripathi NK, Honda K. Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand). Aust J Soil Res. 2003;41(1):47-59. Available:https://doi. org/10.1071/SR02027

Guo PT, Wu W, Sheng QK, Li MF, Lui HB, Wang ZY. Prediction of soil organic matter using artificial neural network and topographic indicators in hilly áreas. Nutr Cyc Agro. 2013;95(3):333-344. Available:

Kolassa J, Reichle RH, Liu Q, Alemohammad SH, Gentine P, Aida K. Estimating surface soil moisture from SMAP observations using a neural network technique. Rem Sens Environ. 2018;204: 43-59. Available:

Ng W, Minasny B, Montazerolghaem M, Padarian J, Ferguson R, Bailey S. Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma. 2019;352:251-267. Available:

Heung B, Ho HC, Zhang J, Knudby A, Bulmer CE, Schmidt MG. An overview and comparison of machine learning techniques for classification purposes in digital soil mapping. Geoderma. 2016;265: 62-77.

Wadoux AMC, Padarian J, Minasny B. Multi-source data integration for soil mapping using deep learning. Soil. 2019;5(1):107-119.

Padarian J, Minasny B, Mc Bratney AB. Using deep learning to predict soil properties from regional spectral data. Geoderma. 2018;15:e00198.

Hillel D. Environmental soil physics academic press. San Diego, CA; 1998.

Pessarakli M, Szabolcs I. Soil salinity and sodicity as particular plant/crop stress factors. In Pessarakli M. (Ed.) Handbook of Plant and Crop Stress; 2002.

Yildirim AN, Şan B, Yildirim F, Celik C, Bayar B, Karakurt Y. Physiological and biochemical responses of almond rootstocks to drought stress. Turkish Journal of Agriculture and Forestry. 2021; 45(4):522-32.

Biazar SM, Ferdosi FB. An investigation on spatial and temporal trends in frost indices in Northern Iran. Theoretical and Applied Climatology. 2020;141(3):907-20.

Jebamalar S, Christopher JJ, Ajisha MA. Random input based prediction and transfer of heat in soil temperature using artificial neural network. Materials Today: Proceedings. 2021;45:1540-6.

Ozturk M, Salman O, Koc M. Artificial neural network model for estimating the soil temperature. Canadian Journal of Soil Science. 2011;91(4):551-62. Available:

Mirzaei-Paiaman, Salavati S. The application of artificial neural networks for the prediction of oil production flow rate, energy sources, part A: Recovery, utilization, and environmental effects. 2012;34:19:1834-1843. DOI: 10.1080/15567036..492386

Trumbore SE, Chadwick OA, Amundson R. Rapid exchange between soil carbon and atmospheric carbon dioxide driven by temperature change. Science. 1996; 272(5260):393-396.

Morteza Amiri, Javad Ghiasi-Freez, Behnam Golkar, Amir Hatampour. Improving water saturation estimation in a tight shaly sandstone reservoir using artificial neural network optimized by imperialist competitive algorithm – A case study. Journal of Petroleum Science and Engineering. 2015;127:347-358.

Tenge AJ, Kaihura FBS, Lal R, Singh BR. Diurnal soil temperature fluctuations for different erosion classes of an oxisol at Mlingano, Tanzania. Soil and Tillage Research. 1998;49(3):211-217.

Biazar SM, Shehadeh HA, Ghorbani MA, Golmohammadi G, Saha A. Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical Grazinglands agro-ecosystems. Scientific Reports. 2024; 14(1):1535.

Jain SK, Singh VP. Modeling soil water retention curve using ANN. Theme D: Hydraulic Parameter Estimation - Remote Sensing; 2003.

Jain SK, Singh VP, Van Genuchten MT. Analysis of soil water retention data using artificial neural networks. Journal of Hydrologic Engineering. 2004;9(5):415-20.

Bharti B, Pandey A, Tripathi SK, Kumar D. Modeling of runoff and sediment yield using ANN, LS-SVR, REP Tree and M5 models. Hydrology Research. 2017;48(6): 1489-1507.

Chatterjee S, Mahapatra SS, Bharadwaj V, Choubey A, Upadhyay BN, Bindra KS. Drilling of micro-holes on titanium alloy using pulsed Nd:YA Glaser: Parametric appraisal and prediction of performance characteristics. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2019;233(8):1872-1889.

Mirzaei-Paiaman, Salavati S. The application of artificial neural networks for the prediction of oil production flow rate, energy sources, part A: Recovery, utilization, and environmental effects. 2012;34:19:1834-1843.

DOI: 10.1080/15567036..492386

Albalasmeh A, Mohawesh O, Gharaibeh M, Deb S, Slaughter L, El Hanandeh A. Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions. Catena. 2022; 217:106459.

Wösten JHM, Pachepsky YA, Rawls WJ. Pedotransfer functions: Bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of hydrology. 2001;251(3-4): 123-150.

Bahrami B, Dianati Tilaki GA, Khosro Beigi S, Janizadeh S, Moetamedi J. Evaluation of artificial neural network (ANN), adative neuro fuzzy inference system (ANFIS) and regression models in prediction of particulate organic matter-carbon (POM-C) in the range lands Kharabe Sanji of Urmia. Applied Soil Research. 2013;1(1):94-106.

Delleur JW, editor. The handbook of groundwater engineering. CRC press; 2006.

Wösten JH, Finke PA, Jansen MJ. Comparison of class and continuous pedotransfer functions to generate soil hydraulic characteristics. Geoderma. 1995; 66(3-4):227-37.

Schaap MG, Leij FJ. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil and Tillage Research. 1998;47(1-2):37-42.

Grubbs RA, Straw CM, Bowling WJ, Radcliffe DE, Taylor Z, Henry GM. Predicting spatial structure of soil physical and chemical properties of golf course fairways using an apparent electrical conductivity sensor. Precision Agriculture. 2019;20:496-519.

Serrano JM, Shahidian S, Marques da Silva J. Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture. Precision Agriculture. 2017;18:245-63.

Uribeetxebarria A, Arnó J, Escolà A, Martínez-Casasnovas JA. Apparent electrical conductivity and multivariate analysis of soil properties to assess soil constraints in orchards affected by previous parcelling. Geoderma. 2018;319: 185-93.

Sanches GM, Magalhães PS, Remacre AZ, Franco HC. Potential of apparent soil electrical conductivity to describe the soil pH and improve lime application in a clayey soil. Soil and Tillage Research. 2018;175:217-25.

Bottega EL, de Queiroz DM, de Assis de Carvalho Pinto F, de Souza CM, Valente DS. Precision agriculture applied to soybean: Part I-delineation of management zones. Australian Journal of Crop Science. 2017;11(5):573-9.

Stockmann U, Adams MA, Crawford JW, Field DJ, Henakaarchchi N, Jenkins M, Minasny B, McBratney AB, De Courcelles VD, Singh K, Wheeler I. The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment. 2013;164:80-99.

Triantafilis J, Odeh IO, McBratney AB. Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Science Society of America Journal. 2001;65(3):869-78.

Ballabio C. Spatial prediction of soil properties in temperate mountain regions using support vector regression. Geoderma. 2009;151(3-4):338-50.

Wang B, Waters C, Orgill S, Cowie A, Clark A, Li Liu D, Simpson M, McGowen I, Sides T. Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecological Indicators. 2018;88: 425-38.

Subburayalu SK, Slater BK. Soil series mapping by knowledge discovery from an Ohio county soil map. Soil Science Society of America Journal. 2013;77(4):1254-68.

Li QQ, Yue TX, Wang CQ, Zhang WJ, Yu Y, Li B, Yang J, Bai GC. Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach. Catena. 2013; 104:210-8.

Malone BP, McBratney AB, Minasny B, Laslett GM. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma. 2009;154(1-2):138-52.

Were K, Bui DT, Dick ØB, Singh BR. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators. 2015;52:394-403.

Zhao Z, Yang Q, Benoy G, Chow TL, Xing Z, Rees HW, Meng FR. Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes. Canadian Journal of Soil Science. 2010;90(1):75-87.

Zhao Z, Chow TL, Rees HW, Yang Q, Xing Z, Meng FR. Predict soil texture distributions using an artificial neural network model. Computers and Electronics in Agriculture. 2009;65(1):36-48.

Kang Y, Li X, Mao D, Wang Z, Liang M. Combining artificial neural network and ordinary kriging to predict wetland soil organic carbon concentration in China’s Liao River Basin. Sensors. 2020;20(24): 7005.

Ushada M, Murase H. Identification of a moss growth system using an artificial neural network model. Biosystems engineering. 2006;94(2):179-189.

Chtioui Y, Panigrahi S, Francl L. A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease. Chemometrics and Intelligent Laboratory Systems. 1999; 48(1): 47-58.

Hossain MA, Uddin MN, Hossain MA, Jang YM. Predicting rice yield for Bangladesh by exploiting weather conditions. In 2017 international conference on information and communication technology convergence (ICTC) 2017; 589-594. IEEE. Available:

Drummond S, Joshi A, Sudduth KA. Application of neural networks: Precision farming. In 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence. 1998;1:211-215.

Liu J, Goering CE, Tian L. A neural network for setting target corn yields. Transactions of the ASAE. 2001;44(3): 705.

Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture. 2010;1;74(1):2-33.

Russello H. Convolutional neural networks for crop yield prediction using satellite images. IBM Center for Advanced Studies; 2018.

Frausto HU, Pieters JG. Modelling greenhouse temperature using system identification by means of neural networks. Neurocomputing. 2004;56:423-8.

Fitz-Rodríguez E, Kubota C, Giacomelli GA, Tignor ME, Wilson SB, McMahon M. Dynamic modeling and simulation of greenhouse environments under several scenarios: A web-based application. Computers and Electronics in Agriculture. 2010;70(1):105-16.

Ushada M, Murase H. Identification of a moss growth system using an artificial neural network model. Biosystems engineering. 2006;94(2):179-189.

Bussab MA, Bernardo JI, Hirakawa AR. Greenhouse modeling using neural networks. In Proc. of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases. 2007; 131-135.

Seginer I, Boulard TH, Bailey BJ. Neural network models of the greenhouse climate. Journal of Agricultural Engineering Research. 1994;59(3):203-16.