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Singh T, Garg NM, Iyengar SRS, Singh V. Near-infrared hyperspectral imaging for determination of protein content in barley samples using convolutional neural network. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01892-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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2
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Xu Y, Wu W, Chen Y, Zhang T, Tu K, Hao Y, Cao H, Dong X, Sun Q. Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds. FRONTIERS IN PLANT SCIENCE 2022; 13:1031849. [PMID: 36523615 PMCID: PMC9745075 DOI: 10.3389/fpls.2022.1031849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.
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Affiliation(s)
- Yanan Xu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Weifeng Wu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Yi Chen
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Tingting Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Keling Tu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Yun Hao
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Hailu Cao
- Hengde Materia Medica (Beijing) Agricultural Technology Co., Ltd., Beijing, China
| | - Xuehui Dong
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Qun Sun
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
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3
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Fu L, Sun J, Wang S, Xu M, Yao K, Cao Y, Tang N. Identification of maize seed varieties based on stacked sparse autoencoder and near‐infrared hyperspectral imaging technology. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lvhui Fu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Simin Wang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Min Xu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Yan Cao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
| | - Ningqiu Tang
- School of Electrical and Information Engineering Jiangsu University Zhenjiang China
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4
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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5
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Singh T, Garg NM, Iyengar SRS. Nondestructive identification of barley seeds variety using near‐infrared hyperspectral imaging coupled with convolutional neural network. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13821] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Tarandeep Singh
- Academy of Scientific and Innovative Research Ghaziabad India
- CSIR‐Central Scientific Instruments Organisation Chandigarh India
| | - Neerja Mittal Garg
- Academy of Scientific and Innovative Research Ghaziabad India
- CSIR‐Central Scientific Instruments Organisation Chandigarh India
| | - S. R. S. Iyengar
- Department of Computer Science and Engineering Indian Institute of Technology Ropar Rupnagar Punjab India
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6
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Feng L, Wu B, Zhu S, He Y, Zhang C. Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins. Front Nutr 2021; 8:680357. [PMID: 34222304 PMCID: PMC8247466 DOI: 10.3389/fnut.2021.680357] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/25/2021] [Indexed: 01/25/2023] Open
Abstract
Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Baohua Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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7
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Wei Y, Li X, Pan X, Li L. Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6980. [PMID: 33297289 PMCID: PMC7731448 DOI: 10.3390/s20236980] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 12/01/2020] [Accepted: 12/04/2020] [Indexed: 11/16/2022]
Abstract
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry and high-performance liquid chromatography) are destructive and time-consuming. In this paper, a robust and accurate method for nondestructive soybean classification is developed through hyperspectral imaging and ensemble machine learning algorithms. Image acquisition, preprocessing, and feature selection are used to obtain different types of soybean hyperspectral features. Based on these features, one of ensemble classifiers-random subspace linear discriminant (RSLD) algorithm is used to classify soybean seeds. Compared with the linear discrimination (LD) and linear support vector machine (LSVM) methods, the results show that the RSLD algorithm in this paper is more stable and reliable. In classifying soybeans in 10, 15, 20, and 25 categories, the RSLD method achieves the highest classification accuracy. When 155 features are used to classify 15 types of soybeans, the classification accuracy of the RSLD method reaches 99.2%, while the classification accuracies of the LD and LSVM methods are only 98.6% and 69.7%, respectively. Therefore, the ensemble classification algorithm RSLD can maintain high classification accuracy when different types and different classification features are used.
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Affiliation(s)
- Yanlin Wei
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; (Y.W.); (L.L.)
- College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
| | - Xiaofeng Li
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; (Y.W.); (L.L.)
| | - Xin Pan
- Changchun Institute of Technology, Changchun 130012, China;
| | - Lei Li
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; (Y.W.); (L.L.)
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Zhang T, Fan S, Xiang Y, Zhang S, Wang J, Sun Q. Non-destructive analysis of germination percentage, germination energy and simple vigour index on wheat seeds during storage by Vis/NIR and SWIR hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 239:118488. [PMID: 32470809 DOI: 10.1016/j.saa.2020.118488] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/26/2020] [Accepted: 05/13/2020] [Indexed: 05/10/2023]
Abstract
Two hyperspectral imaging (HSI) systems, visible/near infrared (Vis/NIR, 304-1082 nm) and short wave infrared (SWIR, 930-2548 nm), were used for the first time to comprehensively predict the changes in quality of wheat seeds based on three vigour parameters: germination percentage (GP, reflecting the number of germinated seedling), germination energy (GE, reflecting the speed and uniformity of seedling emergence), and simple vigour index (SVI, reflecting germination percentage and seedling weight). Each sample contained a small number of wheat seeds, which were obtained by high temperature and humidity-accelerated aging (0, 2, and 3 days) to simulate storage. The spectra of these samples were collected using HSI systems. After collection, each seed sample underwent a standard germination test to determine their GP, GE, and SVI. Then, several pretreatment methods and the partial least-squares regression algorithm (PLS-R) were used to establish quantitative models. The models for the Vis/NIR region obtained excellent performance, and most effective wavelengths (EWs) were selected in the Vis/NIR region by the successive projections algorithm (SPA) and regression coefficients (RC). Subsequently, PLS-R-RC models using selected wavebands (sixteen wavebands for GP, 14 wavebands for GE, and 16 wavebands for SVI) exhibited similar performance to the PLS-R models based on the full wavebands. The best R2 results obtained in the simplified models' prediction sets were 0.921, 0.907, and 0.886, with RMSE values of 4.113%, 5.137%, and 0.024, for GP, GE, and SVI, respectively. Distribution maps of GP, GE, and SVI were produced by applying these simplified PLS models. By interpreting the EWs and building prediction models, soluble protein and sugar content were demonstrated to have a relationship with spectral information. In summary, the present results lay a foundation towards the development of a significantly simpler, more comprehensive, and non-destructive hyperspectral-based sorting system for determining the vigour of wheat seeds.
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Affiliation(s)
- Tingting Zhang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
| | - Yingying Xiang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Shujie Zhang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Jianhua Wang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Qun Sun
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
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9
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Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer. Molecules 2020; 25:molecules25071651. [PMID: 32260173 PMCID: PMC7180573 DOI: 10.3390/molecules25071651] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 04/01/2020] [Accepted: 04/01/2020] [Indexed: 11/16/2022] Open
Abstract
Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky-Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.
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Bai X, Zhang C, Xiao Q, He Y, Bao Y. Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds. RSC Adv 2020; 10:11707-11715. [PMID: 35496579 PMCID: PMC9050551 DOI: 10.1039/c9ra11047j] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 03/02/2020] [Indexed: 11/21/2022] Open
Abstract
Common maize seeds and silage maize seeds are similar in appearance and are difficult to identify with the naked eye. Four varieties of common maize seeds and four varieties of silage maize seeds were identified by near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics. The pixel-wise principal component analysis was used to distinguish the differences among different varieties of maize seeds. The object-wise spectra of each single seed sample were extracted to build classification models. Support vector machine (SVM) and radial basis function neural network (RBFNN) classification models were established using two different classification strategies. First, the maize seeds were directly classified into eight varieties with the prediction accuracy of the SVM model and RBFNN model over 86%. Second, the seeds of silage maize and common maize were firstly classified with the classification accuracy over 88%, then the seeds were classified into four varieties, respectively. The classification accuracy of silage maize seeds was over 98%, and the classification accuracy of common maize seeds was over 97%. The results showed that the varieties of common maize seeds and silage maize seeds could be classified by NIR-HSI combined with chemometrics, which provided an effective means to ensure the purity of maize seeds, especially to isolate common seeds and silage seeds. NIR-HSI and chemometrics were used to identify different varieties of common and silage maize seeds.![]()
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science
- Zhejiang University
- Hangzhou 310058
- China
- Key Laboratory of Spectroscopy Sensing
| | - Chu Zhang
- College of Biosystems Engineering and Food Science
- Zhejiang University
- Hangzhou 310058
- China
- Key Laboratory of Spectroscopy Sensing
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science
- Zhejiang University
- Hangzhou 310058
- China
- Key Laboratory of Spectroscopy Sensing
| | - Yong He
- College of Biosystems Engineering and Food Science
- Zhejiang University
- Hangzhou 310058
- China
- Key Laboratory of Spectroscopy Sensing
| | - Yidan Bao
- College of Biosystems Engineering and Food Science
- Zhejiang University
- Hangzhou 310058
- China
- Key Laboratory of Spectroscopy Sensing
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11
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Zhu S, Zhang J, Chao M, Xu X, Song P, Zhang J, Huang Z. A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning. Molecules 2019; 25:E152. [PMID: 31905957 PMCID: PMC6982693 DOI: 10.3390/molecules25010152] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 12/25/2019] [Accepted: 12/27/2019] [Indexed: 02/02/2023] Open
Abstract
Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds.
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Affiliation(s)
| | | | | | | | | | | | - Zhongwen Huang
- School of Life Science and Technology, Henan Institute of Science and Technology/Henan Collaborative Innovation Center of Modern Biological Breeding, Xinxiang 453003, China; (S.Z.); (J.Z.)
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12
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Feng L, Zhu S, Liu F, He Y, Bao Y, Zhang C. Hyperspectral imaging for seed quality and safety inspection: a review. PLANT METHODS 2019; 15:91. [PMID: 31406499 PMCID: PMC6686453 DOI: 10.1186/s13007-019-0476-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 08/01/2019] [Indexed: 05/22/2023]
Abstract
Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging.
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Affiliation(s)
- Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Susu Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Yidan Bao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, 310058 China
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13
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Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging. Molecules 2018; 23:molecules23112907. [PMID: 30412997 PMCID: PMC6278444 DOI: 10.3390/molecules23112907] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 11/06/2018] [Accepted: 11/07/2018] [Indexed: 11/19/2022] Open
Abstract
Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.
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14
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Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8020212] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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15
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Su WH, Sun DW. Multispectral Imaging for Plant Food Quality Analysis and Visualization. Compr Rev Food Sci Food Saf 2018; 17:220-239. [DOI: 10.1111/1541-4337.12317] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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16
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Gutiérrez S, Fernández-Novales J, Diago MP, Tardaguila J. On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties. FRONTIERS IN PLANT SCIENCE 2018; 9:1102. [PMID: 30090110 PMCID: PMC6068396 DOI: 10.3389/fpls.2018.01102] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 07/09/2018] [Indexed: 05/22/2023]
Abstract
Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions.
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