Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

Zhang, Min; Liu, Zhengying; Zhao, Xinlei; Sun, Shicheng; Li, Xiao; Xu, Chongxuan

doi:10.1177/00037028221104823

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Furthermore, when conducting soil Pb content tests in laboratory conditions, pXRF yields more stable data and provides more accurate data on the number of X-ray-excited electrons collected [ 20 ]. However, the results obtained with pXRF can still be uncertain due to factors such as soil physicochemical properties, element detection limits, interference from similar elements, and challenges in integrating with remote sensing technology, making it difficult to carry out large-scale spatial heavy metal pollution monitoring [ 21 , 22 ]. On the other hand, vis-NIR relies on the collection of visible and near-infrared spectral range light reflected from soil when illuminated with a halogen lamp.…”

Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Zhang,

Wang,

Luo

et al. 2023

Sensors

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Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

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Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Zhang,

Wang,

Luo

et al. 2023

Sensors

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Deep Learning-Based Soil Nutrient Content Prediction for Crop Yield Estimation

Sharma,

Kumar,

Verma

2024

Advances in Electronic Government, Digital Divide, and Regional Development

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This chapter proposes a deep learning-based approach for predicting soil nutrient content and its impact on crop yield. The objective is to develop an accurate model that can assist farmers in making informed decisions about nutrient management and improving crop productivity. The proposed approach employs a combination of a convolutional neural network (CNN) architecture and long short-term memory (LSTM) networks for analyzing soil samples and forecasting nutrient content. Subsequently, the trained model is harnessed to assess the influence of soil nutrient content on crop yield, taking into account factors like climate, water availability, and soil type. The approach was tested on publicly available soil nutrient and crop yield datasets of soil samples collected from different regions and crops. The findings illustrate that the suggested model surpasses conventional approaches and attains remarkable precision in forecasting soil nutrient levels and crop yield.

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Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

Cited by 2 publications

References 34 publications

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Deep Learning-Based Soil Nutrient Content Prediction for Crop Yield Estimation

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