Applications of synchrotron in soil science

Synchrotron radiations (SR) are emerging as a real-time probing tool for the wide range of applied sciences. Since the beginning of 1990s, synchrotron-based techniques have become increasingly employed in various fields of life science. The unique properties of Synchrotron radiations break the limits to characterize the material properties than previous laboratory based techniques. The use of SR in soil sciences also has increased dramatically in the last decade. SR techniques are used to assess soil physical, chemical and biological properties. Besides that SR techniques are also used in soil pollution studies and rhizosphere science. So this paper intends to explain about the instrument synchrotron, its techniques used in soil science and applications in soil science. Furthermore the paper tries to elucidate a few relevant researches in soil science which involves SR techniques.

Exploring the Applicability of Deep Learning Methods in Mid-infrared Spectroscopy for Soil Property Predictions

<p> </p><p>The usage of mathematical models and mid-infrared (MIR) spectral databases to predict the elemental composition of soil allows for rapid and high-throughput characterization of soil properties. The Partial Least Square Regression (PLSR) is a pervasive statistical method that is used for such predictive mathematical models due to a large existing knowledge base paired with standardized best practices in model application. Despite its ability to transform data in the high-dimensional space (high spectral resolution) to a space of fewer dimensions that captures the correlation between the input space (spectra) and the response variables (elemental soil composition), this popular approach fails to capture non-linear patterns. Further, PLSR has poor prediction capacities for a wide range of soil analytes such as Potassium and Phosphorus, just to mention a few. In addition, prediction is highly sensitive to pre-processing steps in data derivation that can also be tainted by human biases based on the empirical selection of wavenumber regions. Thus, the usage of PLSR as a methodology for elemental prediction of soil remains time-consuming and limited in scope.</p><p>With major breakthroughs in the area of Deep Learning (DL) in the past decade, soil science researchers are increasingly shifting their focus from traditional techniques such as PLSR to using DL models such as Convolutional Neural Networks. Promising results of this shift have been showcased, including increased prediction accuracy, reduced needs for data pre-processing, and improved evaluation of explanatory factors. Increasingly, studies are also looking to expand beyond the regional scope and support higher resolution and more accurate databases for global modelling efforts. However, the setup of a DL model is notoriously data intensive and often said to be less applicable when there is limited data available. While a MIR spectra database has been recently publicly released by the Kellog Soil Survey Laboratory, United States Department of Agriculture, such large-scale initiative remains a niche and focus only on specific regions and/or ecosystem types.</p><p>This research is a first effort in applying DL techniques in a relative data scarce environment (approximately 1000 labelled spectra) using transfer learning and domain-specific data augmentation techniques. In particular, we assess the potential of unsupervised feature learning approaches as a key enabler for broader applicability of DL techniques in the context of MIR spectroscopy and soil sciences. A better understanding of potential for DL methods in soil composition prediction will greatly advance the work of soil sciences and natural resource management. Improvements to overcome its associated challenges will be a step forward in creating a universal soil modelling technique through reusable models and contribute to a large world-wide soil MIR spectral database.</p>

“Soil Education Manual - Toolbox for DIY program at your classroom” by International Union of Soil Sciences (IUSS)

<p>Soil education is one of the major topics to be enhanced and promoted in the International Decade of Soils 2015-2024 (IDS) project of the International Union of Soil Sciences (IUSS). The book entitled above has been just published by the IUSS to provide readers, who are interested in soils, geosciences, environment, ecosystems, art, etc. and may be teaching in schools at elementary through university levels, working at museums, educational or extension organizations and serving for NPOs, NGOs, etc., with basic framework of soil and soil science education and a collection of good practices currently employed, so that the readers could learn and share with whatever suited to their own condition efficiently.<br>The book consists of three parts, i.e. framing soil science education, good practices in soil education and future of soil and soil science education. The first part gives tenets and framework of soil education in pre and primary school, under- and post-graduate students and the general public or citizen. The second includes practical methods for soil and soil science education from all over the world, i.e. 1 from Africa, 3 from Asia, 3 from Europe, 2 from North America, 5 from South America and 2 from Oceania, which have been evaluated useful, efficient and promising in their own environments and situations. The final part is devoted for discussing the challenges and future of soil and soil science education. <br>The IUSS is planning to distribute the above publication to a variety of societies so that the current contents and methods and the systems of soil and soil science education be criticized for further improvement towards promoting and enhancing research, education and public awareness of soils as one of the disciplines of geo- and bio-sciences in the future.</p><p> </p>

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

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.

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

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.