数字农科院2.0

Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology

文献类型： 外文期刊

作者： Lijia Xu;Yanjun Chen;Ao Feng;Xiaoshi Shi;Yanqi Feng;Yuping Yang;Yuchao Wang;Zhijun Wu;Zhiyong Zou;Wei Ma;Yong He;Ning Yang;Jing Feng;Yongpeng Zhao

作者机构：

关键词： Farmland soil;Hyperspectral imaging;Microplastic polymers;One-dimensional convolutional neural network

期刊名称： Environmental Research

ISSN： 0013-9351

年卷期： 2023 年 232 卷

页码：

收录情况： SCIE（2023版）

摘要： Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

分类号：

  • 相关文献

[1]A Novel Framework Based on Data Fusion and Machine Learning for Upscaling Evapotranspiration from Flux Towers to the Regional Scale. Pengyuan Zhu,Qisheng Han,Shenglin Li,Hao Liu,Caixia Li,Yanchuan Ma,Jinglei Wang. 2025

[2]Molecular ecological network analysis of the response of soil microbial communities to depth gradients in farmland soils. Wang, Zhong-Liang,Yu, Hang,Zhang, Guilong,Zheng, Wei,Yu, Hang,Xue, Dongmei,Wang, Yidong. 2020

[3]Water-soluble chitosan and phytoremediation efficiency of two Brassica napus L. cultivars in cadmium-contaminated farmland soils. Bian, Jian-Lin,Cao, Wei,Guo, Jun-Mei,Yang, Jun-Xing,Wang, Xue-Dong,Wang, Jie,Huang, Jun,Xia, Tian-Xiang,Xia, Cun-Yan. 2022

[4]Comparative evaluation of soil accumulation of light stabilizers from biodegradable mulching films versus conventional polyethylene ones. Fan R.,Li B.,Liu Q.,Liu Q.,Cui J.,Bai R.,Wang Y.,Elias R.,Li C.,He W.. 2024

[5]Do tillage systems affect the cadmium threshold in farmland soil for environmental quality standard setting?. Zhaolin Du,Weiqiang Dou,Dasong Lin,Li Qin,Yi An,Hongan Chen,Lina Wu,Liyan Mou. 2023

[6]The characteristics and influencing factors of farmland soil microplastic in Hetao Irrigation District, China. Runhao Bai,Hongjin Liu,Jixiao Cui,Yan Wu,Xiaoyu Guo,Qin Liu,Qi Liu,Haihe Gao,Changrong Yan,Wenqing He. 2024

[7]The occurrence and safety evaluation of phthalic acid esters in Oasis agricultural soils of Xinjiang, China. Hejiang Liu,Xiuting Liu,Kai Wang,Xingwang Ma,Haihe Gao,Xuejun Liu,Changrong Yan. 2025

[8]A Spectral Hierarchical Machine Learning for Predicting Arsenic Concentration in Farmland Soil Using Sentinel-2 Imagery. Li Wang , Yong Zhou, Zehan Zhou , Shangrong Wu , Lang Xia , Yan Zha, and Peng Yang. 2025

[9]Optimization Of Fish Quality By E.valuation Of Total Volatile B asic Nitrogen (Tvb-N) And Texture Profile Analysis (Tpa) By Near-Infrared (Nir) Hyperspectral Imaging. Zhao, Junbo,Zhang, Yituo,Wang, Xue,Shan, Jiajia,Han, Shuqing. 2019

[10]Identification Of Adulterated Cooked Millet F.lour With Hyperspectral Imaging A nalysis. Xuan, Guantao,Shao, Yuanyuan,Shao, Yuanyuan,Hu, Zhichao,Gao, Xuemei. 2018

[11]Early Detection Of Aspergillus Parasiticus I.nfection In Maize Kernels U sing Near-Infrared Hyperspectral Imaging And Multivariate Data Analysis. Wang, Wei,Zhao, Xin,Li, Chunyang,Chu, Xuan,Kimuli, Daniel. 2017

[12]Identification of Pummelo Cultivars Based on Hyperspectral Imaging Technology. Li Xun-lan,Li Xun-lan,Yi Shi-lai,He Shao-lan,Lu Qiang,Zheng Yong-qiang,Deng Lie,Yi Shi-lai,He Shao-lan,Lu Qiang,Zheng Yong-qiang,Deng Lie. 2015

[13]Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging. Liu Yanli,Liu Yanli,Lyu Qiang,He Shaolan,Yi Shilai,Liu Xuefeng,Xie Rangjin,Zheng Yongqiang,Deng Lie. 2015

[14]Study on Varieties Identification of Kentucky Bluegrass Using Hyperspectral Imaging and Discriminant Analysis. Xiao Bo,Liang Xiao-hong,Zhang Li-juan,Han Lie-bao,Xiao Bo,Han Lie-bao,Mao Wen-hua. 2012

[15]Research Process on Hyperspectral Imaging Detection Technology for the Quality and Safety of Grain and Oils. Yu Hong-wei,Wang Qiang,Liu Li,Shi Ai-min,Hu Hui,Liu Hong-zhi. 2016

[16]Determination of Photosynthetic Pigments in Citrus Leaves Based on Hyperspectral Images Datas. Tian Xi,Liao Qiu-hong,Tian Xi,He Shao-lan,Lu Qiang,Yi Shi-lai,Xie Rang-jin,Zheng Yong-qiang,Liao Qiu-hong,Deng Lie,He Shao-lan,Lu Qiang,Yi Shi-lai,Xie Rang-jin,Zheng Yong-qiang,Deng Lie. 2014

[17]Detection Of Adulteration With Duck M.eat In Minced Lamb M eat By Using Visible Near-Infrared Hyperspectral Imaging. Zheng, XC, Li, YY, Wei, WS, Peng, YK. 2019

[18]Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Yi Ming Cao,Yan Zhang,Shuang Ting Yu,Kai Kuo Wang,Ying Jie Chen,Zi Ming Xu,Zi Yao Ma,Hong Lu Chen,Qi Wang,Ran Zhao,Xiao Qing Sun,Jiong Tang Li. 2023

[19]A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging. Yuanyuan Shao,Yukang Shi,Yongdong Qin,Guantao Xuan,Jing Li,Quankai Li,Fengjuan Yang,Zhichao Hu. 2022

[20]Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds. Tingting Zhang,Long Lu,Ni Yang,Ian D. Fisk,Wensong Wei,Li Wang,Jing Li,Qun Sun,Rensen Zeng. 2023

作者其他论文 更多>>