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Jeong SW, Lyu JI, Jeong H, Baek J, Moon JK, Lee C, Choi MG, Kim KH, Park YI. SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits. PLANT CELL REPORTS 2024; 43:164. [PMID: 38852113 PMCID: PMC11162974 DOI: 10.1007/s00299-024-03249-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 05/06/2024] [Indexed: 06/10/2024]
Abstract
KEY MESSAGE Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.
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Affiliation(s)
- Seok Won Jeong
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea
| | - Jae Il Lyu
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - HwangWeon Jeong
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jeongho Baek
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jung-Kyung Moon
- Crop Foundation Research Division, National Institute of Crop Sciences, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Chaewon Lee
- Crop Cultivation and Environment Research Division, National Institute of Crop Sciences, 54 Seohoro, Suwon, Kyounggi-do, 16613, Korea
| | - Myoung-Goo Choi
- Wheat Research Team, National Institute of Crop Sciences, RDA, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Kyoung-Hwan Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Youn-Il Park
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea.
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Hwang J, Choi KO, Jeong S, Lee S. Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images. Curr Res Food Sci 2024; 8:100742. [PMID: 38708100 PMCID: PMC11066601 DOI: 10.1016/j.crfs.2024.100742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Hyperspectral imaging analysis combined with machine learning was applied to identify eight edible vegetable oils, and its classification performance was compared with the chemical method based on fatty acid compositions. Furthermore, the degree of adulteration in vegetable oils was quantitatively investigated using machine learning-enabled hyperspectral approaches. The hyperspectral absorbance spectra of palm oil with a high degree of saturation were distinctly different from those of the other liquid oils. The flaxseed and olive oils exhibited the dominant hyperspectral intensities at 1170/1671 and 1212/1415 nm, respectively. Linear discriminant analysis demonstrated that two linear discriminants could explain a significant portion of the total variability, accounting for 96.0% (fatty acid compositions) and 98.9% (hyperspectral images). When the hyperspectral results were used as datasets for three machine learning models (decision tree, random forest, and k-nearest neighbor), several instances to incorrectly classify grapeseed and sunflower oils were detected, while olive, palm, and flaxseed oils were successfully identified. The machine learning models showed a great classification performance that exceeded 98.9% from the hyperspectral images of the vegetable oils, which was comparable to the fatty acid composition-based chemical method in identifying edible vegetable oils. In addition, the random forest model was the most effective in ascertaining adulteration levels in binary oil blends (R2 > 0.992 and RMSE < 2.75).
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Affiliation(s)
- Jeongin Hwang
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
| | - Kyeong-Ok Choi
- Department of Food Science and Technology, Chungnam National University, Daejeon, 34134, South Korea
| | - Sungmin Jeong
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
| | - Suyong Lee
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
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Isik H, Tasdemir S, Taspinar YS, Kursun R, Cinar I, Yasar A, Yasin ET, Koklu M. Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models. Food Sci Nutr 2024; 12:786-803. [PMID: 38370035 PMCID: PMC10867492 DOI: 10.1002/fsn3.3783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 02/20/2024] Open
Abstract
The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short-Term Memory) and BiLSTM (Bi-directional Long Short-Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.
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Affiliation(s)
- Hakan Isik
- Department of Electric‐Electronic EngineeringSelcuk UniversityKonyaTurkey
| | - Sakir Tasdemir
- Department of Computer EngineeringSelcuk UniversityKonyaTurkey
| | | | - Ramazan Kursun
- Guneysinir Vocational SchoolSelcuk UniversityKonyaTurkey
| | - Ilkay Cinar
- Department of Computer EngineeringSelcuk UniversityKonyaTurkey
| | - Ali Yasar
- Department of Mechatronic EngineeringSelcuk UniversityKonyaTurkey
| | | | - Murat Koklu
- Department of Computer EngineeringSelcuk UniversityKonyaTurkey
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Franco C, Osorio M, Peyre G. Automatic seed classification for four páramo plant species by neural networks and optic RGB images. NEOTROPICAL BIODIVERSITY 2023. [DOI: 10.1080/23766808.2022.2161243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Affiliation(s)
- Camilo Franco
- Department of Industrial Engineering, University of the Andes, Bogotá, Colombia
| | - Manuela Osorio
- Department of Civil and Environmental Engineering, University of the Andes, Bogotá, Colombia
| | - Gwendolyn Peyre
- Department of Civil and Environmental Engineering, University of the Andes, Bogotá, Colombia
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Weng S, Ma J, Tao W, Tan Y, Pan M, Zhang Z, Huang L, Zheng L, Zhao J. Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion. FRONTIERS IN PLANT SCIENCE 2023; 14:1073530. [PMID: 36925753 PMCID: PMC10011179 DOI: 10.3389/fpls.2023.1073530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Drought stress (DS) is one of the most frequently occurring stresses in tomato plants. Detecting tomato plant DS is vital for optimizing irrigation and improving fruit quality. In this study, a DS identification method using the multi-features of hyperspectral imaging (HSI) and subsample fusion was proposed. First, the HSI images were measured under imaging condition with supplemental blue lights, and the reflectance spectra were extracted from the HSI images of young and mature leaves at different DS levels (well-watered, reduced-watered, and deficient-watered treatment). The effective wavelengths (EWs) were screened by the genetic algorithm. Second, the reference image was determined by ReliefF, and the first four reflectance images of EWs that are weakly correlated with the reference image and mutually irrelevant were obtained using Pearson's correlation analysis. The reflectance image set (RIS) was determined by evaluating the superposition effect of reflectance images on identification. The spectra of EWs and the image features extracted from the RIS by LeNet-5 were adopted to construct DS identification models based on support vector machine (SVM), random forest, and dense convolutional network. Third, the subsample fusion integrating the spectra and image features of young and mature leaves was used to improve the identification further. The results showed that supplemental blue lights can effectively remove the high-frequency noise and obtain high-quality HSI images. The positive effect of the combination of spectra of EWs and image features for DS identification proved that RIS contains feature information pointing to DS. Global optimal classification performance was achieved by SVM and subsample fusion, with a classification accuracy of 95.90% and 95.78% for calibration and prediction sets, respectively. Overall, the proposed method can provide an accurate and reliable analysis for tomato plant DS and is hoped to be applied to other crop stresses.
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Avuçlu E, Taşdemir Ş, Köklü M. A new hybrid model for classification of corn using morphological properties. Eur Food Res Technol 2022. [DOI: 10.1007/s00217-022-04181-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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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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8
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Kabir MH, Guindo ML, Chen R, Liu F, Luo X, Kong W. Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27186042. [PMID: 36144775 PMCID: PMC9501738 DOI: 10.3390/molecules27186042] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/09/2022] [Accepted: 09/12/2022] [Indexed: 11/19/2022]
Abstract
Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.
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Affiliation(s)
- Muhammad Hilal Kabir
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Department of Agricultural and Bio-Resource Engineering, Abubakar Tafawa Balewa University, Bauchi PMB 0248, Nigeria
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-571-88982825
| | - Xinmeng Luo
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, China
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Tu K, Wen S, Cheng Y, Xu Y, Pan T, Hou H, Gu R, Wang J, Wang F, Sun Q. A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning. PLANT METHODS 2022; 18:81. [PMID: 35690826 PMCID: PMC9188178 DOI: 10.1186/s13007-022-00918-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/31/2022] [Indexed: 05/24/2023]
Abstract
BACKGROUND Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. RESULTS Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. CONCLUSIONS This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.
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Affiliation(s)
- Keling Tu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Shaozhe Wen
- Beijing Key Laboratory of Vegetable Germplasm Improvement, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Beijing, 100097, People's Republic of China
| | - Ying Cheng
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Yanan Xu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Tong Pan
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Haonan Hou
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Riliang Gu
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Jianhua Wang
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China
| | - Fengge Wang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Center, Beijing Academy of Agriculture and Forestry Sciences (BAAFS), Beijing, 100097, People's Republic of China.
| | - Qun Sun
- Department of Plant Genetics & Breeding and Seed Science, College of Agronomy and Biotechnology, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds Whole-Process Technology Research, Beijing, 100193, People's Republic of China.
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Zhang J, Wang Z, Qu M, Cheng F. Research on physicochemical properties, microscopic characterization and detection of different freezing-damaged corn seeds. Food Chem X 2022; 14:100338. [PMID: 35634222 PMCID: PMC9133772 DOI: 10.1016/j.fochx.2022.100338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 04/19/2022] [Accepted: 05/18/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
| | | | | | - Fang Cheng
- Corresponding author at: Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
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11
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Applications of machine learning in pine nuts classification. Sci Rep 2022; 12:8799. [PMID: 35614118 PMCID: PMC9132955 DOI: 10.1038/s41598-022-12754-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/16/2022] [Indexed: 11/09/2022] Open
Abstract
Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.
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Liu Q, Wang Z, Long Y, Zhang C, Fan S, Huang W. Variety classification of coated maize seeds based on Raman hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120772. [PMID: 34973616 DOI: 10.1016/j.saa.2021.120772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/18/2021] [Accepted: 12/13/2021] [Indexed: 05/27/2023]
Abstract
As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm-1 were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield.
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Affiliation(s)
- Qingyun Liu
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Zuchao Wang
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China
| | - Yuan Long
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Chi Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
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Gao T, Chandran AKN, Paul P, Walia H, Yu H. HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds. SENSORS (BASEL, SWITZERLAND) 2021; 21:8184. [PMID: 34960287 PMCID: PMC8703337 DOI: 10.3390/s21248184] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/27/2021] [Accepted: 12/04/2021] [Indexed: 01/04/2023]
Abstract
High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.
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Affiliation(s)
- Tian Gao
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
| | - Anil Kumar Nalini Chandran
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; (A.K.N.C.); (P.P.); (H.W.)
| | - Puneet Paul
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; (A.K.N.C.); (P.P.); (H.W.)
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA; (A.K.N.C.); (P.P.); (H.W.)
| | - Hongfeng Yu
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;
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Hu Q, Zhang Y, Ma R, An J, Huang W, Wu Y, Hou J, Zhang D, Lin F, Xu R, Sun Q, Sun L. Genetic dissection of seed appearance quality using recombinant inbred lines in soybean. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:72. [PMID: 37309518 PMCID: PMC10236129 DOI: 10.1007/s11032-021-01262-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 11/01/2021] [Indexed: 06/14/2023]
Abstract
Soybean seed appearance quality greatly affects the marketability. The objective of this study was to identify the quantitative trait loci (QTLs) that control the appearance quality of soybean seeds. A total of 256 recombinant inbred lines from Qi Huang No.34 × Ji Dou No.17 were utilized for QTL mapping. We innovatively applied a machine vision system to quantify the seed appearance of each line. As a result of QTL mapping, a total of 145 QTLs for the machine vision parameters were detected across three environments. We integrated QTLs mapped overlapped and obtained 16 QTL hotspots in total. Of these hotspots, hotspot-4-1 was suggested to be a major locus controlling seed size, and hotspot-15 was identified to affect the seed color and texture. The mapping for principal components of the seed appearance also supported it. This study comprehensively dug up the QTLs for seed appearance quality of soybean cultivars while providing an efficient method for phenotyping of seed appearance. These results would contribute to dissecting the genetic bases of seed appearance quality for the improvement of soybean. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01262-9.
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Affiliation(s)
- Quan Hu
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Yanwei Zhang
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250131 Shandong China
| | - Ruirui Ma
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Jie An
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Wenxuan Huang
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Yueying Wu
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Jingjing Hou
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Dajian Zhang
- College of Agronomy, Shandong Agricultural University, Tai’an, 271018 Shandong China
| | - Feng Lin
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824 USA
| | - Ran Xu
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250131 Shandong China
| | - Qun Sun
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
| | - Lianjun Sun
- State Key Laboratory of Agrobiotechnology, and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
- Beijing Key Laboratory for Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193 China
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Zhang J, Xu B, Wang Z, Cheng F. Application of hyperspectral imaging in the detection of aflatoxin B1 on corn seed. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-01171-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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16
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Zhou Q, Huang W, Tian X, Yang Y, Liang D. Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:4532-4542. [PMID: 33452811 DOI: 10.1002/jsfa.11095] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/08/2021] [Accepted: 01/16/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed. RESULTS First, visible and near-infrared (NIR-visible) hyperspectral images were obtained. Savitzky-Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non-embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non-embryoid forms was 97.78% and 98.15%, respectively. CONCLUSION The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Quan Zhou
- School of Electronics and Information Engineering, Anhui University, Hefei, China
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Wenqian Huang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Xi Tian
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Yi Yang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Dong Liang
- School of Electronics and Information Engineering, Anhui University, Hefei, China
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
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Rathna Priya TS, Manickavasagan A. Characterising corn grain using infrared imaging and spectroscopic techniques: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-00898-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Li FL, Xie J, Wang S, Wang Y, Xu CH. Direct qualitative and quantitative determination methodology for massive screening of DON in wheat flour based on multi-molecular infrared spectroscopy (MM-IR) with 2T-2DCOS. Talanta 2021; 234:122653. [PMID: 34364462 DOI: 10.1016/j.talanta.2021.122653] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 12/20/2022]
Abstract
Deoxynivalenol (DON) contamination in wheat flour induces a number of adverse health effects to consumers and livestock, even at very low concentrations. Direct detection methods for massive screening of DON in wheat flour is still lacking. A new methodology integrating multi-molecular infrared spectroscopy (MM-IR) with two-trace two-dimensional correlation spectroscopy (2T-2DCOS) was developed for in-situ qualitative and quantitative determination of DON in wheat flour as a whole. Typical spectral variation of wheat flour samples with diverse concentration of DON were stepwise characterized by MM-IR and tiny spectral profile differences resulting from concentration variation of DON were visually disclosed by 2T-2DCOS. Based on the obtained key spectral features of DON, 180 of wheat flour samples with DON higher and lower than 1.00 mg/kg were undoubtedly classified by Principal Component Analysis (PCA) and Support Vector Machines (SVM) with an accuracy rate up to 100% (for Second derivative spectra consisted of selected bands, SD-SS). Furthermore, a robust quantitative prediction model was established based on partial least squares (PLS) of SD-SS (Rc: 0.998, RMSEC: 0.135; Rp: 0.968, RMSEP: 0.421), and its excellent predictive capacity of model was validated by both residual prediction deviation (RPD) value of 3.2 and t-test. It was demonstrated that the developed methodology was applicable for screening and quantitative detection of DON in wheat flour based on the novel correlation analysis methods (SD-2DCOS-IR and 2T-2DCOS-IR) with chemometrics tools, which could be utilized both at laboratory and industrial level for quality control purposes of a large wheat flour sample set.
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Affiliation(s)
- Fei-Li Li
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai, 201306, China
| | - Jun Xie
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai, 201306, China
| | - Song Wang
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Qinpu Biotechnology Pte Ltd, Shanghai, 201306, China
| | - Yang Wang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300112, China
| | - Chang-Hua Xu
- College of Food Science & Technology, Shanghai Ocean University, Shanghai, 201306, China; Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, Shanghai, 201306, China; Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai, 201306, China; National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Shanghai, 201306, China.
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Liu W, Zeng S, Wu G, Li H, Chen F. Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model. SENSORS (BASEL, SWITZERLAND) 2021; 21:4384. [PMID: 34206783 PMCID: PMC8271842 DOI: 10.3390/s21134384] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 11/25/2022]
Abstract
Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67-100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60-100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.
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Affiliation(s)
- Weihua Liu
- School of Electric & Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China;
| | - Shan Zeng
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China; (H.L.); (F.C.)
| | - Guiju Wu
- The Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430023, China;
| | - Hao Li
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China; (H.L.); (F.C.)
| | - Feifei Chen
- School of Mathematics & Computer Science, Wuhan Polytechnic University, Wuhan 430023, China; (H.L.); (F.C.)
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20
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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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21
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22
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Li Y, Al-Sarayreh M, Irie K, Hackell D, Bourdot G, Reis MM, Ghamkhar K. Identification of Weeds Based on Hyperspectral Imaging and Machine Learning. FRONTIERS IN PLANT SCIENCE 2021; 11:611622. [PMID: 33569069 PMCID: PMC7868399 DOI: 10.3389/fpls.2020.611622] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 12/30/2020] [Indexed: 06/12/2023]
Abstract
Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70-100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.
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Affiliation(s)
- Yanjie Li
- AgResearch Ltd., Grasslands Research Centre, Palmerston North, New Zealand
| | | | - Kenji Irie
- Red Fern Solutions Ltd, Christchurch, New Zealand
| | - Deborah Hackell
- AgResearch Ltd., Ruakura Research Centre, Hamilton, New Zealand
| | | | - Marlon M. Reis
- AgResearch Ltd., Grasslands Research Centre, Palmerston North, New Zealand
| | - Kioumars Ghamkhar
- AgResearch Ltd., Grasslands Research Centre, Palmerston North, New Zealand
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23
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Bantan RAR, Ali A, Naeem S, Jamal F, Elgarhy M, Chesneau C. Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods. CHAOS (WOODBURY, N.Y.) 2020; 30:113142. [PMID: 33261340 DOI: 10.1063/5.0024017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/14/2020] [Indexed: 06/12/2023]
Abstract
The purpose of this study is to discriminate sunflower seeds with the help of a dataset having spectral and textural features. The production of crop based on seed purity and quality other hand sunflower seed used for oil content worldwide. In this regard, the foundation of a dataset categorizes sunflower seed varieties (Syngenta CG, HS360, S278, HS30, Armani, and High Sun 33), which were acquired from the agricultural farms of The Islamia University of Bahawalpur, Pakistan, into six classes. For preprocessing, a new region-oriented seed-based segmentation was deployed for the automatic selection of regions and extraction of 53 multi-features from each region, while 11 optimized fused multi-features were selected using the chi-square feature selection technique. For discrimination, four supervised classifiers, namely, deep learning J4, support vector machine, random committee, and Bayes net, were employed to optimize the multi-feature dataset. We observe very promising accuracies of 98.2%, 97.5%, 96.6%, and 94.8%, respectively, when the size of a region is (180 × 180).
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Affiliation(s)
- Rashad A R Bantan
- Department of Marine Geology, Faculty of Marine Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia
| | - Aqib Ali
- Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur 61300, Pakistan
| | - Samreen Naeem
- Department of Computer Science & IT, Glim Institute of Modern Studies, Bahawalpur 61300, Pakistan
| | - Farrukh Jamal
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Punjab 63100, Pakistan
| | - Mohammed Elgarhy
- Valley High Institute for Management Finance and Information Systems, Obour, Qaliubia 11828, Egypt
| | - Christophe Chesneau
- Department of Mathematics, Université de Caen, LMNO, Campus II, Science 3, 14032 Caen, France
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24
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Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01871-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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25
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Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2020. [DOI: 10.1007/s11694-020-00646-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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26
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Li H, Jiang D, Cao J, Zhang D. Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20174905. [PMID: 32872634 PMCID: PMC7506848 DOI: 10.3390/s20174905] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 06/11/2023]
Abstract
Lipid content is an important indicator of the edible and breeding value of Pinus koraiensis seeds. Difference in origin will affect the lipid content of the inner kernel, and neither can be judged by appearance or morphology. Traditional chemical methods are small-scale, time-consuming, labor-intensive, costly, and laboratory-dependent. In this study, near-infrared (NIR) spectroscopy combined with chemometrics was used to identify the origin and lipid content of P. koraiensis seeds. Principal component analysis (PCA), wavelet transformation (WT), Monte Carlo (MC), and uninformative variable elimination (UVE) methods were used to process spectral data and the prediction models were established with partial least-squares (PLS). Models were evaluated by R2 for calibration and prediction sets, root mean standard error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). Two dimensions of input data produced a faster and more accurate PLS model. The accuracy of the calibration and prediction sets was 98.75% and 97.50%, respectively. When the Donoho Thresholding wavelet filter 'bior4.4' was selected, the WT-MC-UVE-PLS regression model had the best predictions. The R2 for the calibration and prediction sets was 0.9485 and 0.9369, and the RMSECV and RMSEP were 0.0098 and 0.0390, respectively. NIR technology combined with chemometric algorithms can be used to characterize P. koraiensis seeds.
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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.
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Zhang D, Chen G, Zhang H, Jin N, Gu C, Weng S, Wang Q, Chen Y. Integration of spectroscopy and image for identifying fusarium damage in wheat kernels. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 236:118344. [PMID: 32330824 DOI: 10.1016/j.saa.2020.118344] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/26/2020] [Accepted: 04/05/2020] [Indexed: 05/20/2023]
Abstract
Hyperspectral imaging (HSI) was studied for the detection of varying degrees of damage in wheat kernels caused by Fusarium head blight (Gibberella zeae), a major disease in wheat worldwide. A total of 810 wheat kernel samples were collected from a field trial with the three levels of Fusarium infection, healthy, moderate, and severe. Hyperspectral image of the wheat kernels was acquired over a wavelength range of 400-1000 nm. The raw spectral data were pre-processed, and then the optimal wavelengths were selected using principal component analysis (PCA), successive projection algorithm (SPA) and random forest (RF). The image features were extracted based on the optimal wavelengths, and then the spectral features and image features were combined as fusion features. Support vector machine (SVM), random forest (RF) and naive Bayes (NB) were employed to build the classification models to identify the degrees of Fuasrium damage based on spectral and fusion features. The best performance was obtained by using the SPA-RF method to select the optimal wavelengths and corresponding image features, with a classification accuracy of 96.44%. The method developed from this study can provide a more effective way to identify the degrees of Fusarium damage in wheat kernels.
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Affiliation(s)
- Dongyan Zhang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Gao Chen
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Huihui Zhang
- Water Management and Systems Research Unit, USDA Agricultural Research Service, Fort Collins, CO, 80526, USA
| | - Ning Jin
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; Department of Resources and Environment, Shanxi Institute of Energy, Jinzhong 030600, China
| | - Chunyan Gu
- Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Qian Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
| | - Yu Chen
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China; Institute of Plant Protection and Agro-products Safety, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
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Zhao J, Fang Y, Chu G, Yan H, Hu L, Huang L. Identification of Leaf-Scale Wheat Powdery Mildew ( Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier. PLANTS 2020; 9:plants9080936. [PMID: 32722022 PMCID: PMC7464903 DOI: 10.3390/plants9080936] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/13/2020] [Accepted: 07/22/2020] [Indexed: 11/20/2022]
Abstract
Powdery mildew (PM, Blumeria graminis f. sp. tritici) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.
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Affiliation(s)
- Jinling Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
- Correspondence: (J.Z.); (L.H.)
| | - Yan Fang
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Guomin Chu
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Hao Yan
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Lei Hu
- School of Electronics and Information Engineering, Anhui University, Hefei 230601, China; (Y.F.); (G.C.); (H.Y.); (L.H.)
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
- Correspondence: (J.Z.); (L.H.)
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Liu J, Liu S, Shi T, Wang X, Chen Y, Liu F, Men H. A modified feature fusion method for distinguishing seed strains using hyperspectral data. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2020. [DOI: 10.1515/ijfe-2019-0362] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
AbstractPrecise classification of seeds is important for agriculture. Due to the slight physical and chemical difference between different types of wheat and high correlation between bands of images, it is easy to fall into the local optimum when selecting the characteristic band of using the spectral average only. In this paper, in order to solve this problem, a new variable fusion strategy was proposed based on successive projection algorithm and the variable importance in projection algorithm to obtain a comprehensive and representative variable feature for higher classification accuracy, within spectral mean and spectral standard deviation, so the 25 feature bands obtained are classified by support vector machine, and the classification accuracy rate reached 83.3%. It indicates that the new fusion strategy can mine the effective features of hyperspectral data better to improve the accuracy of the model and it can provide a theoretical basis for the hyperspectral classification of tiny kernels.
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Affiliation(s)
- Jingjing Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
- Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, 30602, GA, USA
- Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Simeng Liu
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
| | - Tie Shi
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
| | - Xiaonan Wang
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China
| | - Yizhou Chen
- Department of Neurobiology and Behavior, University of California, Irvine, 92697, CA, USA
| | - Fulong Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China
| | - Hong Men
- College of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China
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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] [Grants] [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.
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Affiliation(s)
- Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University Hangzhou 310058 China +86-571-88982143 +86-571-88982143
- 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 +86-571-88982143 +86-571-88982143
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs Hangzhou 310058 China
| | - Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University Hangzhou 310058 China +86-571-88982143 +86-571-88982143
- 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 +86-571-88982143 +86-571-88982143
- 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 +86-571-88982143 +86-571-88982143
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs Hangzhou 310058 China
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Hruska Z, Yao H, Kincaid R, Tao F, Brown RL, Cleveland TE, Rajasekaran K, Bhatnagar D. Spectral-Based Screening Approach Evaluating Two Specific Maize Lines With Divergent Resistance to Invasion by Aflatoxigenic Fungi. Front Microbiol 2020; 10:3152. [PMID: 32038584 PMCID: PMC6988685 DOI: 10.3389/fmicb.2019.03152] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 12/29/2019] [Indexed: 11/13/2022] Open
Abstract
In an effort to control aflatoxin contamination in food and/or feed grains, a segment of research has focused on host resistance to eliminate aflatoxin from susceptible crops, including maize. To this end, screening tools are key to identifying resistant maize genotypes. The traditional field screening techniques, the kernel screening laboratory assay (KSA), and analytical methods (e.g., ELISA) used for evaluating corn lines for resistance to fungal invasion, all ultimately require sample destruction. A technological advancement on the basic BGYF presumptive screening test, fluorescence hyperspectral imaging offers an option for non-destructive and rapid image-based screening. The present study aimed to differentiate fluorescence spectral signatures of representative resistant and susceptible corn hybrids infected by a toxigenic (SRRC-AF13) and an atoxigenic (SRRC-AF36) strain of Aspergillus flavus, at several time points (5, 7, 10, and 14 days), in order to evaluate fluorescence hyperspectral imaging as a viable technique for early, non-invasive aflatoxin screening in resistant and susceptible corn lines. The study utilized the KSA to promote fungal growth and aflatoxin production in corn kernels inoculated under laboratory conditions and to provide actual aflatoxin values to relate with the imaging data. Each time point consisted of 78 kernels divided into four groups (30-susceptible, 30-resistant, 9-susceptible control, and 9-resistant control), per inoculum. On specified days, kernels were removed from the incubator and dried at 60°C to terminate fungal growth. Dry kernels were imaged with a VNIR hyperspectral sensor (image spectral range of 400–1000 nm), under UV excitation centered at 365 nm. Following imaging, kernels were submitted for the chemical AflaTest assay (VICAM). Fluorescence emissions were compared for all samples over 14 days. Analysis of strain differences separating the fluorescence emission peaks of resistant from the susceptible strain indicated that the emission peaks of the resistant strain and the susceptible strains differed significantly (p < 0.01) from each other, and there was a significant difference in fluorescence intensity between the treated and control kernels of both strains. These results indicate a viable role of fluorescence hyperspectral imaging for non-invasive screening of maize lines with divergent resistance to invasion by aflatoxigenic fungi.
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Affiliation(s)
- Zuzana Hruska
- Geosystems Research Institute, Mississippi State University, MSU Science and Technology, Stennis Space Center, Starkville, MS, United States
| | - Haibo Yao
- Geosystems Research Institute, Mississippi State University, MSU Science and Technology, Stennis Space Center, Starkville, MS, United States
| | - Russell Kincaid
- Geosystems Research Institute, Mississippi State University, MSU Science and Technology, Stennis Space Center, Starkville, MS, United States
| | - Feifei Tao
- Geosystems Research Institute, Mississippi State University, MSU Science and Technology, Stennis Space Center, Starkville, MS, United States
| | - Robert L Brown
- Southern Regional Research Center, USDA-ARS, New Orleans, LA, United States
| | - Thomas E Cleveland
- Southern Regional Research Center, USDA-ARS, New Orleans, LA, United States
| | | | - Deepak Bhatnagar
- Southern Regional Research Center, USDA-ARS, New Orleans, LA, United States
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Femenias A, Gatius F, Ramos AJ, Sanchis V, Marín S. Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review. Food Control 2020. [DOI: 10.1016/j.foodcont.2019.106819] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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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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35
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Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods. Eur Food Res Technol 2019. [DOI: 10.1007/s00217-019-03419-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Zhu S, Chao M, Zhang J, Xu X, Song P, Zhang J, Huang Z. Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology. SENSORS (BASEL, SWITZERLAND) 2019; 19:E5225. [PMID: 31795146 PMCID: PMC6929038 DOI: 10.3390/s19235225] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/21/2019] [Accepted: 11/26/2019] [Indexed: 11/16/2022]
Abstract
Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.
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Affiliation(s)
| | | | | | | | | | | | - Zhongwen Huang
- School of Life Science and Technology, Henan Institute of Science and Technology/Collaborative Innovation Center of Modern Biological Breeding of Henan Province, Xinxiang 453003, China; (S.Z.)
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Hyperspectral Image Classification Based on Spectral and Spatial Information Using Multi-Scale ResNet. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9224890] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. In this paper, to make full use of HSI information, we combined spectral and spatial information into a two-dimension image in a particular order by extracting a data cube and unfolding it. Prior to the step of combining, principle component analysis (PCA) is utilized to decrease the dimensions of HSI so as to reduce computational cost. Moreover, the classification block used during the experiment is a convolutional neural network (CNN). Instead of using traditionally fixed-size kernels in CNN, we leverage a multi-scale kernel in the first convolutional layer so that it can scale to the receptive field. To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. Extensive experiments on the datasets from Pavia University and Salinas demonstrate that the proposed method significantly improves the accuracy in HSI classification.
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Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties. Molecules 2019; 24:molecules24183268. [PMID: 31500333 PMCID: PMC6766998 DOI: 10.3390/molecules24183268] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 09/02/2019] [Accepted: 09/06/2019] [Indexed: 01/17/2023] Open
Abstract
Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.
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Classical and emerging non-destructive technologies for safety and quality evaluation of cereals: A review of recent applications. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.07.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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40
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Zhang Y, Guo W. Moisture content detection of maize seed based on visible/near‐infrared and near‐infrared hyperspectral imaging technology. Int J Food Sci Technol 2019. [DOI: 10.1111/ijfs.14317] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yanmin Zhang
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling Shaanxi 712100 China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering Northwest A&F University Yangling Shaanxi 712100 China
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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: 55] [Impact Index Per Article: 11.0] [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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Mirzaei M, Marofi S, Abbasi M, Solgi E, Karimi R, Verrelst J. Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2019; 80:26-37. [PMID: 36081710 PMCID: PMC7613368 DOI: 10.1016/j.jag.2019.04.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classification algorithms (LDA and SVM) were applied in a total of 16 scenarios. Results showed that the grapevine varieties were discriminated with overall accuracy of 89.88%-100% in test sets. Among the data reduction methods, the combination of ANOVA and PCA yielded higher performance as opposed to PLSR. Accordingly, optimal wavelengths in discrimination of studied grapevine varieties were located in vicinity of 695, 752, 1148, 1606 nm and 582, 687, 1154, 1927 nm at leaf and canopy levels, respectively. Optimal spectral indices were R680, WI, SGB and RATIO975_2, DattA, Greenness at leaf and canopy levels, respectively. Also, the importance of spectral regions in discriminating studied grapevine varieties was ranked as near-infrared > mid-infrared and red edge region > visible. As a general conclusion, the canopyspectral indices-ANOVA-PCA-SVM scenario discriminated the studied species most accurately.
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Affiliation(s)
- Mohsen Mirzaei
- Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Islamic Republic of Iran
| | - Safar Marofi
- Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Islamic Republic of Iran
| | - Mozhgan Abbasi
- Faculty of Natural Resource and Earth Science, Shahrekord University, Islamic Republic of Iran
| | - Eisa Solgi
- Faculty of Natural Resource and Environment, Malayer University, Islamic Republic of Iran
| | - Rholah Karimi
- Green Space Design group, Faculty of Agriculture, Malayer University, Islamic Republic of Iran
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980, Paterna, València, Spain
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Ru C, Li Z, Tang R. A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI). SENSORS 2019; 19:s19092045. [PMID: 31052476 PMCID: PMC6539508 DOI: 10.3390/s19092045] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/18/2019] [Accepted: 04/29/2019] [Indexed: 01/07/2023]
Abstract
Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435-1042 nm) and short-wave infrared (SWIR, 898-1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs).
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Affiliation(s)
- Chenlei Ru
- Department of Industrial and Systems Engineering, Zhejiang University, Hangzhou 310058, China.
| | - Zhenhao Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
| | - Renzhong Tang
- Department of Industrial and Systems Engineering, Zhejiang University, Hangzhou 310058, China.
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Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9081530] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.
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Zhang J, Dai L, Cheng F. Classification of Frozen Corn Seeds Using Hyperspectral VIS/NIR Reflectence Imaging. Molecules 2019; 24:E149. [PMID: 30609734 PMCID: PMC6337657 DOI: 10.3390/molecules24010149] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 12/26/2018] [Accepted: 12/27/2018] [Indexed: 11/20/2022] Open
Abstract
A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.
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Affiliation(s)
- Jun Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Limin Dai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
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Al-Saddik H, Laybros A, Simon JC, Cointault F. Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of "Flavescence Dorée". Methods Mol Biol 2019; 1875:213-238. [PMID: 30362007 DOI: 10.1007/978-1-4939-8837-2_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Flavescence Dorée (FD) is a contagious and incurable grapevine disease that can be perceived on leaves. In order to contain its spread, the regulations obligate winegrowers to control each plant and to remove the suspected ones. Nevertheless, this monitoring is performed during the harvest and mobilizes many people during a strategic period for viticulture. To solve this problem, we aim to develop a Multi-Spectral (MS) imaging device ensuring an automated grapevine disease detection solution. If embedded on a UAV, the tool can provide disease outbreaks locations in a geographical information system allowing localized and direct treatment of infected vines. The high-resolution MS camera aims to allow the identification of potential FD occurrence, but the procedure can, more generally, be used to detect any type of foliar diseases on any type of vegetation.Our work consists on defining the spectral bands of the multispectral camera, responsible for identifying the desired symptoms of the disease. In fact, the FD diseased samples were selected after establishing a Polymerase Chain Reaction (PCR) confirmation test and then a feature selection technique was applied to identify the best subset of wavelengths capable of detecting FD samples. An example of a preliminary version of the MS sensor was also presented along with the geometric and radiometric required corrections. An image analysis based on texture and neural networks was also detailed for an enhanced disease classification.
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Affiliation(s)
- H Al-Saddik
- Agroecology, Agrosup Dijon, INRA, Univ. Bourgogne Franche-Comté, Dijon, France.
| | - A Laybros
- Agroecology, Agrosup Dijon, INRA, Univ. Bourgogne Franche-Comté, Dijon, France
| | - J C Simon
- Agroecology, Agrosup Dijon, INRA, Univ. Bourgogne Franche-Comté, Dijon, France
| | - F Cointault
- Agroecology, Agrosup Dijon, INRA, Univ. Bourgogne Franche-Comté, Dijon, France
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Miao A, Zhuang J, Tang Y, He Y, Chu X, Luo S. Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis. SENSORS (BASEL, SWITZERLAND) 2018; 18:E4391. [PMID: 30545028 PMCID: PMC6308723 DOI: 10.3390/s18124391] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 12/07/2018] [Accepted: 12/08/2018] [Indexed: 01/22/2023]
Abstract
Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7⁻1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher's discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.
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Affiliation(s)
- Aimin Miao
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
| | - Jiajun Zhuang
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
| | - Yu Tang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Xuan Chu
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
| | - Shaoming Luo
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.
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48
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Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8101793] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Near-infrared (874–1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.
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Zhang T, Wei W, Zhao B, Wang R, Li M, Yang L, Wang J, Sun Q. A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. SENSORS 2018. [PMID: 29517991 PMCID: PMC5876662 DOI: 10.3390/s18030813] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.
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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.
| | - Wensong Wei
- National R&D Center for Agro-Processing Equipments, College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Bin Zhao
- 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.
| | - Ranran 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.
| | - Mingliu Li
- 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.
| | - Liming Yang
- College of Science, China Agricultural University, Beijing 100083, 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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50
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Zheng Y, Bai J, Xu J, Li X, Zhang Y. A discrimination model in waste plastics sorting using NIR hyperspectral imaging system. WASTE MANAGEMENT (NEW YORK, N.Y.) 2018; 72:87-98. [PMID: 29129466 DOI: 10.1016/j.wasman.2017.10.015] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 09/29/2017] [Accepted: 10/12/2017] [Indexed: 05/09/2023]
Abstract
Classification of plastics is important in the recycling industry. A plastic identification model in the near infrared spectroscopy wavelength range 1000-2500 nm is proposed for the characterization and sorting of waste plastics using acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The model is built by the feature wavelengths of standard samples applying the principle component analysis (PCA), and the accuracy, property and cross-validation of the model were analyzed. The model just contains a simple equation, center of mass coordinates, and radial distance, with which it is easy to develop classification and sorting software. A hyperspectral imaging system (HIS) with the identification model verified its practical application by using the unknown plastics. Results showed that the identification accuracy of unknown samples is 100%. All results suggested that the discrimination model was potential to an on-line characterization and sorting platform of waste plastics based on HIS.
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Affiliation(s)
- Yan Zheng
- Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China
| | - Jiarui Bai
- Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China
| | - Jingna Xu
- Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China
| | - Xiayang Li
- Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China
| | - Yimin Zhang
- Key Laboratory for Green Chemical Technology of State Education Ministry, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, PR China; Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300350, PR China.
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