Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy

Deiss, Leonardo; Margenot, Andrew J.; Culman, Steve W.; Demyan, Michael Scott

doi:10.1016/j.geoderma.2020.114227

Cited by 86 publications

(49 citation statements)

References 31 publications

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“…Different studies can return different results since the nature of the target function has a strong influence on the performance of the different prediction approaches (Friedman, 2001;Viscarra Rossel and Behrens, 2010). Previous studies reported variable results depending on the soil property and sample set (Deiss et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

Wijewardane

Sanderman

et al. 2021

Soil Science Soc of Amer J

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Section: Introductionmentioning

confidence: 99%

Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

Wijewardane

Sanderman

et al. 2021

Soil Science Soc of Amer J

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“…To overcome these restrictions, the application of machine learning algorithms, such as random forest (RF) [41][42][43][44][45], artificial neural network (ANN) [23,26,29,40,46,47], and support vector regression (SVR) [11,48,49] algorithms, has gradually developed in soil science research due to the significant advantages of these algorithms over previous approaches, i.e., improved model accuracy, greater computing efficiency, and simplified fitting. Due to the spatial variability in climatic conditions, soil parent materials, human activities, and land use management, no single algorithm model is universally applicable for predicting SOM in different regions [50,51].…”

Section: Introductionmentioning

confidence: 99%

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Zhang

Yang

et al. 2021

Remote Sensing

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Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-precision and high-resolution SOM maps remains a challenge due to the difficulty of selecting appropriate satellite data sources and prediction algorithms. This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction. We constructed two comparative experiments, i.e., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE). The predictive performances of random forest (RF), artificial neural network (ANN), and support vector regression (SVR) algorithms were evaluated, and the SOM map was generated for the Songnen Plain. Results showed that the model based on the full-band Sentinel-2A dataset achieved the best performance. The application of Sentinel-2A data resulted in mean relative improvements (RIs) of 7.67% and 5.87%, respectively. The RF achieved a lower root mean squared error (RMSE = 0.68%) and a higher coefficient of determination (R2 = 0.67) in all of the predicted scenarios than ANN and SVR. The resultant SOM map accurately characterized the SOM spatial distribution. Therefore, the Sentinel-2A data have obvious advantages over MODIS due to their higher spectral and spatial resolutions, and the combination of the RF algorithm and GEE is an effective approach to SOM mapping.

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“…These results suggest a good suitability of the SVM approach for the above-described C and N parameters. Findings of other authors [29,42] who used and compared SVM with linear methods for MIRS modelling generally support these results, although they were not obtained for organic materials but for soil samples with far smaller C and N contents.…”

Section: Calibration Of Pmirs Svm Prediction Models For Particle Sizementioning

confidence: 72%

“…Moreover, the spectral response of OA can vary widely and limit the performance of linear models such as plsr because factors such as material origin, particle size distribution, fermentation conditions, or pyrolytic decomposition during biochar production and therefore the chemical composition during biochar production vary to a high degree. A computational approach to overcome these interfering influences is the use of machine learning methods [29]. Because of their ability to determine complex and non-linear relationships, these methods became popular in several research fields.…”

Section: Introductionmentioning

confidence: 99%

Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines

2021

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Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc’s) of >4 mm, 2–4 mm, 0.5–2 mm, and <0.5 mm. A universal SVM model was then developed for all OA and psc’s (n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts.

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Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy

Cited by 86 publications

References 31 publications

Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines

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