Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

Nie, Ping; Dong, Tao; He, Yong; Qu, Fangfang

doi:10.3390/s17051102

Cited by 50 publications

(42 citation statements)

References 23 publications

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“…Considering that the Raman peaks of TBZ in red soil extracts were mainly distributed in the range of 300–1700 cm −1 , in this paper, the 300–1700 cm −1 SERS of 84 samples were obtained and then pretreated by Savitzky-Golay smoothing (S-G) [ 28 ], standard normal variation (SNV) [ 29 ] and multiplicative scatter correction (MSC) [ 30 ], respectively. Meanwhile, the original and preprocessed SERS were modeled by partial least squares (PLS) [ 31 ] and backward interval partial least squares (biPLS) [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

et al. 2018

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Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm−1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm−1: y = 672.26x + 5748.4, R2 = 0.9948; 1008 cm−1: y = 1155.4x + 8740.2, R2 = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300–1700 cm−1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp2 = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.

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Section: Resultsmentioning

confidence: 99%

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

et al. 2018

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“…Nie et al detected a soil nitrogen content prepared with three different pretreatment methods by near infrared sensor. It was proved that the soil with the strictest pretreatment (dried, ground, sieved, and pressed) had the highest accuracy in predicting the soil nitrogen content [ 8 ]. Antonios Morellos et al improved the algorithm accuracy by means of two multivariate methods including principal component regression (PCR) and partial least squares regression (PLSR), and two machine learning methods including least squares support vector machines (LS-SVM) and Cubist, revealing that the Cubist method provided the best [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Xiao

Nie

et al. 2017

Sensors

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Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

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“…Recent research activity has been focused on many different IoT-related topics such as event forecasting [ 8 ], WSN routing protocols [ 9 ], multi-sensor information fusion [ 10 ], business model and profit maximization [ 11 ], ontologies [ 12 ], service models [ 13 ], quality of experience [ 14 ], and even advanced concepts for the prioritization of raw data processing and information dissemination such as Quality of Information (QoI) [ 15 ] and Value-of-Information [ 16 ]. Researchers have also developed a multitude of application-specific solutions for issues in diagnostics [ 17 , 18 ], environmental monitoring [ 19 , 20 ], and social interest [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities

Bellavista

Giannelli

Lanzone

et al. 2017

Sensors

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The spread of off-the-shelf mobile devices equipped with multiple wireless interfaces together with sophisticated sensors is paving the way to novel wireless Internet of Things (IoT) environments, characterized by multi-hop infrastructure-less wireless networks where devices carried by users act as sensors/actuators as well as network nodes. In particular, the paper presents Real Ad-hoc Multi-hop Peer-to peer-Wireless IoT Application (RAMP-WIA), a novel solution that facilitates the development, deployment, and management of applications in sparse Smart City environments, characterized by users willing to collaborate by allowing new applications to be deployed on their smartphones to remotely monitor and control fixed/mobile devices. RAMP-WIA allows users to dynamically configure single-hop wireless links, to manage opportunistically multi-hop packet dispatching considering that the network topology (together with the availability of sensors and actuators) may abruptly change, to actuate reliably sensor nodes specifically considering that only part of them could be actually reachable in a timely manner, and to upgrade dynamically the nodes through over-the-air distribution of new software components. The paper also reports the performance of RAMP-WIA on simple but realistic cases of small-scale deployment scenarios with off-the-shelf Android smartphones and Raspberry Pi devices; these results show not only the feasibility and soundness of the proposed approach, but also the efficiency of the middleware implemented when deployed on real testbeds.

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Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

Cited by 50 publications

References 23 publications

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities

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