Main Article Content

pallavi goranthala


The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyses soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (Latent Dirichlet Allocation) to find patterns in a large collection of text corpus. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that: a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, b) the reviewed publication can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, SVM), spectroscopy, modelling (classes), crops, physical and modelling (continuous), c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular: about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.



Download data is not yet available.


Metrics Loading ...

Article Details

How to Cite
goranthala, pallavi, ASAM DINESH, GOGU SWATHI, SAMA ABHIGNYA, and MADARAPU GOWTHAM GANESH GRE. “SOIL PROPERTIES PREDICTION USING MACHINE LEARNING TECHNIQUES”. Technix International Journal for Engineering Research, vol. 9, no. 7, July 2022, pp. 21-26,
Research Articles


Amal, U.C.; Isang, I.A. Status and spatial variability of soil properties in relation to fertilizer placement

for intercrops in an oil palm plantation in Calabar, Nigeria. Niger. J. Crop Sci. 2018, 5, 58–72.

Akpan, J.F.; Aki, E.E.; Isang, I.A. Comparative assessment of wetland and coastal plain soils in Calabar,

Cross River State. Glob. J. Agric. Sci. 2017, 16, 17–30. [Crossruff]

Jenny, H. Factors of Soil Formation: A System of Quantitative Pedology, 1st ed.; McGraw-Hill Inc.: New

York, NY, USA, 1941.

Chimezie, I.A.; Eswaran, H.; Asawa am, D.O.; Aon, A.O. Characterization of two benchmark soils of

contrasting parent materials in Abie State, Southeastern Nigeria. Glob. J. Pure Appl. Sci. 2010, 16, 23–29.


Amal, U.C.; Isang, I.A. Land capability and soil suitability of some acid sand soil supporting oil palm (El

aegis Guinness Jacq) trees in Calabar, Nigeria. Niger. J. Soil Sci. 2015, 25, 92–109.

Taghizadeh-Mehrjardi, R.; Zabiullah, K.; Kerry, R. Digital mapping of soil organic carbon at multiple

depths using different data mining techniques in Bane region, Iran. Ganoderma 2016, 266, 98–110.


Bian, Z.; Guo, X.; Wang, S.; Zhuang, Q.; Jinn, X.; Wang, Q.; Jia, S. Applying statistical methods to map

soil organic carbon of agricultural lands in northeastern coastal areas of China. Arch. Argon. Soil Sci. 2020,

, 532–544. [crossed]

Chen, L.; Ren, C.; Li, L.; Wang, Y.; Zhang, B.; Wang, Z.; Li, L. A Comparative Assessment of

Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content.

ISPRS Int. J. Geo-Information 2019, 8, 174.

Kingsley, J.; Lawani, S.O.; Esther, A.O.; Ndiaye, K.M.; Sunday, O.J.; Peniel, V. Predictive Mapping of

Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Enstatite’s

Models. Mod. Appl. Sci. 2019, 13, 60.

Mosleh, Z.; Salehi, M.; Jafari, A.; Esfandiarpour, I.; Mehnatkesh, A. The effectiveness of digital soil

mapping to predict soil properties over low-relief areas. Environ. Monit. Assess. 2016, 188, 195.

Most read articles by the same author(s)