A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM

Li, Yanru; Yang, Keming; Gao, Wei; Han, Qianqian; Zhang, Jianhong

doi:10.1016/j.saa.2021.119649

Cited by 24 publications

(5 citation statements)

References 24 publications

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“…SVR is the regression method of SVM, the idea of SVR has been described by Smola and Schölkopf ( Smola and Schölkopf, 2004 ). In SVR, the mapping of input data in higher-order feature space is accomplished by several types of kernel functions ( Li et al., 2021 ), such as linear, nonlinear, sigmoid, polynomial, and radial basis functions (RBFs). Among various kernel functions, the RBF kernel can achieve good results.…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Tuerxun,

Zheng,

Wang

et al. 2023

Front. Plant Sci.

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The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

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Section: Methodsmentioning

confidence: 99%

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Tuerxun,

Zheng,

Wang

et al. 2023

Front. Plant Sci.

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“…PCA was a classical dimension reduction method, which could select some decorrelated variables that represent most of the information of the original data (Li, Yang, Gao, Han, & Zhang, 2021). In detail, PCA projected the original variables to the new coordinates.…”

Section: Methodsmentioning

confidence: 99%

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Sun

Zhang

Zhou

et al. 2021

J Food Process Engineering

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Accurate, rapid, and nondestructive identification of rice seed varieties has great significance for agriculture and food security, a method based on information fusion and artificial fish swarm algorithm (AFSA) combined with the hyperspectral imaging (HSI) of five kinds of rice seeds was proposed in this work. First, the spectral and image data were obtained from HSI, and the spectral data were preprocessed by detrending. Then, bootstrapping soft shrinkage (BOSS), variable iterative space shrinkage approach, successive projections algorithm, and principal component analysis were adopted to select feature variables from the spectral and image data. Next, the support vector machine (SVM) model was constructed based on the spectral and image feature variables. In order to further improve the classification accuracy of single feature model, the model based on fused feature was developed and it was finally optimized by AFSA. The research showed that the feature variables (114 spectral variables and 11 image variables) selected by BOSS were representative, and the model accuracy based on BOSS spectral and image features reached 91.48% and 70%, respectively. The performance of the SVM model based on fused feature was improved significantly, and the model accuracy reached 97.22%. After AFSA optimization, the model accuracy finally reached 99.44%. The above result confirmed that using AFSA to optimize the model based on fused feature could be a promising method to identify rice seed varieties. Practical application Rapid and accurate identification of rice seed varieties contributes to establishment of online rice seed identification system. HSI combined with information fusion and AFSA can overcome the disadvantage of low accuracy of traditional nondestructive testing methods. The method proposed in this article can be recommended to be widely popularized in farms, grain markets, and market regulators.

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“…The current research directions in heavy metal pollution are divided into predicting the concentration of heavy metals in crops and classifying different classes of heavy metals for detection. The model developed in the above study has achieved good accuracy [ 88 , 60 ]. However, all the above studies established traditional machine learning models based on the spectral features of crops.…”

Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM

Cited by 24 publications

References 24 publications

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

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