Next Article in Journal
Channel and Timeslot Co-Scheduling with Minimal Channel Switching for Data Aggregation in MWSNs
Next Article in Special Issue
Optical Sensing to Determine Tomato Plant Spacing for Precise Agrochemical Application: Two Scenarios
Previous Article in Journal
Design of a Single Channel Modulated Wideband Converter for Wideband Spectrum Sensing: Theory, Architecture and Hardware Implementation
Previous Article in Special Issue
A Wireless Sensor Network for Growth Environment Measurement and Multi-Band Optical Sensing to Diagnose Tree Vigor
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 1036;

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & College of Life Sciences and Oceanography, Shenzhen University, 518060 Shenzhen, China
Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China
School of Resource and Environmental Sciences, Wuhan University, 430079 Wuhan, China
Suzhou Institute of Wuhan University, 215000 Suzhou, China
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios Moshou
Received: 24 March 2017 / Revised: 24 April 2017 / Accepted: 2 May 2017 / Published: 4 May 2017
(This article belongs to the Special Issue Sensors in Agriculture)
PDF [3648 KB, uploaded 4 May 2017]


This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. View Full-Text
Keywords: visible and near-infrared reflectance spectroscopy; heavy metal contamination; spectral pre-processing; feature selection; machine-learning visible and near-infrared reflectance spectroscopy; heavy metal contamination; spectral pre-processing; feature selection; machine-learning

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Shi, T.; Liu, H.; Chen, Y.; Fei, T.; Wang, J.; Wu, G. Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils. Sensors 2017, 17, 1036.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top