Coping with imbalanced data problem in digital mapping of soil classes

An unsolved problem in the digital mapping of categorical soil variables and soil types is the imbalanced number of observations, which leads to reduced accuracy and the loss of the minority class (the class with a significantly lower number of observations compared to other classes) in the final map. So far, synthetic over- and under-sampling techniques have been explored in soil science; however, more efficient approaches that do not have the drawbacks of these techniques and guarantee retention of the minority classes in the produced map are essentially required. Such approaches suggested in the present study for digital mapping of soil classes include machine learning models of ensemble gradient boosting, cost-sensitive learning and one-class classification (OCC) of the minority class combined with multi-class classification. In this regard, extreme gradient boosting (XGB) as an ensemble gradient learner, a cost-sensitive decision tree (CSDT) within the C5.0 algorithm, and a one-class support vector machine combined with multi-class classification (OCCM) were investigated to map eight soil great groups with a naturally imbalanced frequency of observations in northwest Iran. A total of 453 profile data points were used for mapping the soil great groups of the study area. A data split was done manually for each class separately, which resulted in an overall 70% of the data for calibration and 30% for validation. The bootstrapping approach of calibration (with 10 runs) was performed to produce multiple maps for each model. The 10 bootstraps were evaluated against the hold-out validation dataset. The average values of accuracy measures, including Kappa (K), overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), were explored. In addition, the results of this study were compared with a previous study in the same area, in which resampling techniques were used to deal with imbalanced data for digital soil class mapping. The findings show that all three suggested methods can deal well with the imbalanced classification problem, with OCCM showing the highest K (= 0.76) and OA (= 82) in the validation stage. Also, this model can guarantee the retention of the minority classes in the final map. Comparing the present approaches with the previous study approach demonstrates that the three newly suggested methods can remarkably increase both overall and individual class accuracy for mapping.     

Lateral and Vertical Variations of Soil Organic and Inorganic Carbon Content in Aridisols and Entisols of a Rangeland

Eurasian Soil Science - The influence of biological and physicochemical soil properties on the variations in soil organic and inorganic carbon (OC and IC) contents at the soil surface was studied....     

Essential oil and methanolic extract of Zataria multiflora Boiss with anticholinesterase effect

One of the most common strategies in the treatment of cognitive disorders is enhancing the acetylcholine level in the brain through the inhibition of acetylcholinesterase. Despite the effectiveness of current modern drugs, more attention has been paid for finding new anticholinesterase agents from medicinal plants. Zatraia multiflora Boiss. is an endemic plant to Iran which has different uses in traditional medicine as anti-inflammatory, antimicrobial, anti spasmodic. We intended to evaluate the in vitro anticholinesterase and free radical scavenging activity of the essential oil and methanolic extract of Z. multiflora. The essential oil and methanolic extract of the plant were evaluated for anticholinesterase activity using modified Ellman method. The free radical scavenging effect of the samples were studied by using of the diphenylpicrylhydrazyl (DPPH). IC50 and the percent of inhibition of acetylcholinesterase was calculated from regression equation. The results showed that both the essential oil and methanolic extract of the plant exhibited high anticholinesterase activity (95.3 +/- 3.4 and 87.9 +/- 2.2% inhibition, respectively) which was similar to eserine (96.2 +/- 1.7% inhibition). The IC50 value of essential oil was determined as 0.97 +/- 0.12 microg mL(-1) in comparison to eserine (0.13 +/- 0.02 microg mL(-1)). The results of antioxidant assay showed that both the essential oil and methanolic extract potentially inhibit DPPH free radical (94.8 +/- 2.4 and 93.2 +/- 1.7% inhibition, respectively). The essential oil and methanolic extract of Z. multiflora have beneficial effect in health promotion and this plant would be good candidate for further studies.     

Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec^TM (ASD) and NeoSpectra^TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R² = 0.89 (RMSE = 0.12%) and R² = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R² = 0.75 (RMSE = 0.45%) and R² = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.