Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning

Massawe, Boniface H. J.; Subburayalu, Sakthi Kumaran; Kaaya, Abel K.; Winowiecki, Leigh A.; Slater, Brian K.

doi:10.1016/j.geoderma.2016.11.020

Cited by 28 publications

(22 citation statements)

References 26 publications

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“…Modelling (classes): Articles focused on the modelling, especially mapping, of categorical soil properties based on their relationship with environmental covariates (Mansuy et al, 2014;Camera et al, 2017;Dharumarajan et al, 2017;Massawe et al, 2018). In this category is also possible to find articles related to the use of conventional soil maps, especially spatial disaggregation of polygons (Subburayalu et al, 2014;Vincent et al, 2018;Flynn et al, 2019).…”

Section: Methods (Nn Svm)mentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

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Abstract. 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 analyse 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.

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Section: Methods (Nn Svm)mentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…While the average sample is composed of 1,000 units, about one third of the studies use a sample with less than 150 units, mostly for local or small-scale regional areas. For example, Blanco et al (2018) use a sample of size 47 for mapping soil water retention in a 93 km 2 area while Massawe et al (2018) observed 33 soil profiles to calibrate a ML algorithm and to predict soil taxa over a 11,600 km 2 area.…”

Section: Sampling Design Sample Size and Densitymentioning

confidence: 99%

Machine learning for digital soil mapping: Applications, challenges and suggested solutions

Wadoux

Minasny

McBratney

2020

Earth-Science Reviews

297

115

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“…Over the last years, the AI-based techniques have been proposed in various areas of research, such as meteorological, hydrological and soil sciences (Tabari et al, 2011;Talaee, 2014;Aitkenhead and Coull, 2016;Liu et al, 2016;Citakoglu, 2017;Mehdizadeh et al, 2017aMehdizadeh et al, , 2018aGavili et al, 2018;Massawe et al, 2018;Singh et al, 2018;Maroufpoor et al, 2019;Azad et al, 2020;Mehdizadeh, 2020). For example, the AI approaches were applied successfully by Mehdizadeh (2018a), Mehdizadeh et al (2017b) and Azad et al (2020) to estimate the dew point and air temperatures.…”

Section: Introductionmentioning

confidence: 99%

Modelling daily soil temperature at different depths via the classical and hybrid models

Mehdizadeh

Ahmadi

Sales³

2020

Meteorological Applications

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Soil temperature (ST) is one of the crucial variables of soil and it plays a fundamental role in different research scopes such as underground soil physical and agricultural applications. The study explores the modelling performance of a time series‐based model (i.e. bi‐linear, BL), and an artificial intelligence‐based approach including adaptive neuro‐fuzzy inference system (ANFIS), for modelling the daily ST of different soil depths (5, 10, 50 and 100 cm). The study also develops and proposes two diverse types of the hybrid models through coupling the ANFIS with the BL and wavelet analysis (W) to improve the accuracy of the ST modelling. Two stations in Iran (i.e. Isfahan and Urmia) were selected as the study locations. The results demonstrated that the ANFIS generally presented better results than the BL. Furthermore, the hybrid models (i.e. W‐ANFIS and ANFIS‐BL) gave superior performances than the classical ANFIS and BL for modelling the daily ST of the studied areas at various soil depths. In addition to the local evaluation of the ANFIS (i.e. modelling the ST at a specific depth by using the original ST data at that depth), an external analysis was also conducted. In doing so, the daily ST data at a 5 cm depth were modelled via the corresponding ST data at a 10 cm depth, and vice versa. The results denoted the applicability of the ST data at another depth for modelling the ST of each specific/target depth.

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Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning

Cited by 28 publications

References 26 publications

Machine learning and soil sciences: A review aided by machine learning tools

Machine learning and soil sciences: A review aided by machine learning tools

Machine learning for digital soil mapping: Applications, challenges and suggested solutions

Modelling daily soil temperature at different depths via the classical and hybrid models

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