Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map

Flynn, Trevan; Rozanov, Andrei; Clercq, Willem de; Warr, Benjamin; Clarke, Catherine E.

doi:10.1016/j.geoderma.2018.11.003

Cited by 24 publications

(5 citation statements)

References 38 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

“…et al, 2017; Massawe et al, 2018). In this category it 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: Main Topicsmentioning

confidence: 99%

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

Padarian

Minasny

McBratney

2020

SOIL

236

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 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 corpora. 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 publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (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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“…(2020) used a probabilistic model to report the key research areas in soil science that utilizes ML techniques. Main topics included soil salinity (Khadim et al., 2019), soil organic C dynamics, soil moisture content (Greifeneder et al., 2019), pedometrics timeline (McBratney et al., 2019), spatial disaggregation (Flynn et al., 2019), soil mapping and modeling regional soil covariates (Caubet et al., 2019; Padarian et al., 2020), and soil physicochemical properties (Khadim et al., 2019; Lamb & Brown, 2001; Viscarra Rossel & McBratney, 1998). Regenerative agriculture, permaculture, and precision farming are overarching tools to attain sustainable goals of food production.…”

Section: Integrative Permaculture: Fusion Of Data Sciences (Artificia...mentioning

confidence: 99%

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

et al. 2021

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Intensive agriculture can cause a loss in biodiversity and concerns for global food security. Regenerative agriculture and integrative permaculture are keys to address food security. Digital agricultural tools can aid in attaining agricultural and environmental sustainability. Precision agriculture should be implemented as an agricultural decision support system. Artificial intelligence and machine learning can guide integrated agricultural input management.

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Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map

Cited by 24 publications

References 38 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 and soil sciences: a review aided by machine learning tools

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

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