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Zou Z, Wang Q, Wu Q, Li M, Zhen J, Yuan D, Zhou M, Xu C, Wang Y, Zhao Y, Yin S, Xu L. Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 355:120503. [PMID: 38457894 DOI: 10.1016/j.jenvman.2024.120503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/16/2024] [Accepted: 02/25/2024] [Indexed: 03/10/2024]
Abstract
The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.
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Affiliation(s)
- Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Menghua Li
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Dongyu Yuan
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Man Zhou
- College of Food Science, Sichuan Agricultural University, Ya'an, 625014, China
| | - Chong Xu
- Ruijie Networks Co., Ltd., Chengdu, 610000, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China
| | - Shutao Yin
- Institute of Modern Agricultural Industry, China Agricultural University, Chengdu, Sichuan, 611430, China.
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China.
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Bai R, Zhou J, Wang S, Zhang Y, Nan T, Yang B, Zhang C, Yang J. Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning. Foods 2024; 13:498. [PMID: 38338633 PMCID: PMC10855119 DOI: 10.3390/foods13030498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model's classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model's accuracy. Upon validating the model's performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.
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Affiliation(s)
- Ruibin Bai
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Junhui Zhou
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Siman Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Yue Zhang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Tiegui Nan
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Bin Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Jian Yang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China; (R.B.); (J.Z.); (S.W.); (Y.Z.); (T.N.); (B.Y.)
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Kang Z, Fan R, Zhan C, Wu Y, Lin Y, Li K, Qing R, Xu L. The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning. Molecules 2024; 29:682. [PMID: 38338424 PMCID: PMC10856461 DOI: 10.3390/molecules29030682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
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Affiliation(s)
- Zhiliang Kang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rongsheng Fan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Chunyi Zhan
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Youli Wu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Yi Lin
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Kunyu Li
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Rui Qing
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (Z.K.); (R.F.); (C.Z.); (Y.W.); (Y.L.); (K.L.); (R.Q.)
- Sichuan Research Center for Smart Agriculture Engineering Technology, Ya’an 625000, China
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Zou Z, Chen J, Wu W, Luo J, Long T, Wu Q, Wang Q, Zhen J, Zhao Y, Wang Y, Chen Y, Zhou M, Xu L. Detection of peanut seed vigor based on hyperspectral imaging and chemometrics. FRONTIERS IN PLANT SCIENCE 2023; 14:1127108. [PMID: 36923124 PMCID: PMC10010490 DOI: 10.3389/fpls.2023.1127108] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
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Affiliation(s)
- Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jie Chen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Weijia Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jinghao Luo
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Tao Long
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongming Chen
- School of Electrical Engineering and Automation, Hubei Normal University, Huangshi, Hubei, China
| | - Man Zhou
- Food Academy, Sichuan Agricultural University, Yaan, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
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Wu Q, Xu L, Zou Z, Wang J, Zeng Q, Wang Q, Zhen J, Wang Y, Zhao Y, Zhou M. Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models. FRONTIERS IN PLANT SCIENCE 2022; 13:1047479. [PMID: 36438117 PMCID: PMC9685660 DOI: 10.3389/fpls.2022.1047479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
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Affiliation(s)
- Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jian Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qifeng Zeng
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Man Zhou
- College of Food Sciences, Sichuan Agricultural University, Yaan, China
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