Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination

Sánchez-Esteva, Sara; Knadel, Maria; Kucheryavskiy, Sergey; Jonge, Lis Wollesen de; Rubæk, G. H.; Hermansen, Cecilie; Heckrath, G.

doi:10.3390/s20185419

Cited by 24 publications

(13 citation statements)

References 92 publications

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“…NIR and Raman spectroscopy were integrated in a system to detect additives during PP production . In recent years, a combination of spectroscopic sensors is prevailing in the medical industry and soil elements detection, , while it was not appealing in solid waste sorting. From the results of practices, single-sensor classification has already achieved nearly 100% accuracy; hence, performance of combination was less attractive.…”

Section: Sensor Combinations For Solid Waste Sorting Systemsmentioning

confidence: 99%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

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Section: Sensor Combinations For Solid Waste Sorting Systemsmentioning

confidence: 99%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

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“…Theoretically, the SD, data range, and distribution pattern affect the accuracy of the regression analysis. Compared to previous studies (121-991 mg P kg -1 ) [54], our data set included a larger variance of Pox values, which can be expected to improve the predictive ability of a model. studies (121-991 mg P kg -1 ) [54], our data set included a larger variance of Pox values, which can be expected to improve the predictive ability of a model.…”

Section: Data Handling and Implementationmentioning

confidence: 99%

“…Compared to previous studies (121-991 mg P kg -1 ) [54], our data set included a larger variance of Pox values, which can be expected to improve the predictive ability of a model. studies (121-991 mg P kg -1 ) [54], our data set included a larger variance of Pox values, which can be expected to improve the predictive ability of a model. In the present study, PLS and RF models were implemented using R ver.…”

Section: Data Handling and Implementationmentioning

confidence: 99%

Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

et al. 2021

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As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

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“…Competitive adaptive reweighted sampling (CARS) is a feature selection algorithm based on the absolute regression coefficients (RC) of partial least squares regression (PLSR) [45]. The principle of CARS is identifying wavelengths with large RC by adaptive reweighted sampling (ARS) method and exponential attenuation function (EDF), and then select a series of variable subsets with minimum root mean square error of crossvalidation (RMSECV).…”

Section: Feature Selectionmentioning

confidence: 99%

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Jiang

et al. 2021

Agriculture

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The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.

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Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination

Cited by 24 publications

References 92 publications

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

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