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Liu F, Yang R, Chen R, Lamine Guindo M, He Y, Zhou J, Lu X, Chen M, Yang Y, Kong W. Digital techniques and trends for seed phenotyping using optical sensors. J Adv Res 2024; 63:1-16. [PMID: 37956859 PMCID: PMC11380022 DOI: 10.1016/j.jare.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 10/19/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. AIM OF REVIEW This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.
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Affiliation(s)
- Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mahamed Lamine Guindo
- 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
| | - Jun Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
| | - Xiangyu Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Mengyuan Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Yinhui Yang
- 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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Singh T, Garg NM, Iyengar SRS, Singh V. Near-infrared hyperspectral imaging for determination of protein content in barley samples using convolutional neural network. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01892-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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3
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Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2062944. [PMID: 35990122 PMCID: PMC9385333 DOI: 10.1155/2022/2062944] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/16/2022] [Accepted: 06/28/2022] [Indexed: 12/24/2022]
Abstract
Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.
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An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
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Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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5
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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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Reddy P, Guthridge KM, Panozzo J, Ludlow EJ, Spangenberg GC, Rochfort SJ. Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview. SENSORS 2022; 22:s22051981. [PMID: 35271127 PMCID: PMC8914962 DOI: 10.3390/s22051981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 11/30/2022]
Abstract
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.
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Affiliation(s)
- Priyanka Reddy
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - Kathryn M. Guthridge
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - Joe Panozzo
- Agriculture Victoria Research, 110 Natimuk Road, Horsham, VIC 3400, Australia;
- Centre for Agriculture Innovation, University of Melbourne, Parkville, VIC 3010, Australia
| | - Emma J. Ludlow
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
| | - German C. Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Simone J. Rochfort
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia; (P.R.); (K.M.G.); (E.J.L.); (G.C.S.)
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
- Correspondence:
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7
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Zhang L, Nie Q, Ji H, Wang Y, Wei Y, An D. Hyperspectral imaging combined with generative adversarial network (GAN)-based data augmentation to identify haploid maize kernels. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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8
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Fatemi A, Singh V, Kamruzzaman M. Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy. Food Chem 2022; 383:132442. [PMID: 35182865 DOI: 10.1016/j.foodchem.2022.132442] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 11/19/2022]
Abstract
Many studies have been conducted using NIR spectroscopy to predict corn constituents; however, a systematic investigation of the spectral sub-regions under the scope of overtones and combinations has not been performed. In this study, the corn spectra were divided into second overtones (1100-1388 nm), first overtones (1390-1852 nm), and combinations (1852-2498 nm). Then, using variable importance in projection and genetic algorithm, each region was inspected sequentially to identify the most informative sub-region for each attribute to improve interpretability. The identified spectral subsets were further tuned to select the most influential bands for each attribute. The sub-regions in combinations bands was most informative for predicting water (1908-2108 nm, 2 bands), oil (2176-2304 nm, 6 bands), and protein (2130-2190 nm, 3 bands), whereas the first overtones region was the best for predicting starch (1452-1770 nm, 5 bands). Results provided valuable information for potential hardware and software improvements.
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Affiliation(s)
- Ali Fatemi
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Vijay Singh
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Mohammed Kamruzzaman
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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9
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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10
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Zhang L, Sun H, Li H, Rao Z, Ji H. Identification of rice-weevil (Sitophilus oryzae L.) damaged wheat kernels using multi-angle NIR hyperspectral data. J Cereal Sci 2021. [DOI: 10.1016/j.jcs.2021.103313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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11
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Sun H, Zhang L, Li H, Rao Z, Ji H. Nondestructive identification of barley seeds varieties using hyperspectral data from two sides of barley seeds. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Heng Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education, China Agricultural University Beijing China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture China Agricultural University Beijing China
| | - Liu Zhang
- Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education, China Agricultural University Beijing China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture China Agricultural University Beijing China
| | - Hao Li
- Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education, China Agricultural University Beijing China
- College of Information and Electrical Engineering China Agricultural University China
| | - Zhenhong Rao
- College of Science China Agricultural University Beijing China
| | - Haiyan Ji
- Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education, China Agricultural University Beijing China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture China Agricultural University Beijing China
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12
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Singh T, Garg NM, Iyengar SRS. Nondestructive identification of barley seeds variety using near‐infrared hyperspectral imaging coupled with convolutional neural network. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13821] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Tarandeep Singh
- Academy of Scientific and Innovative Research Ghaziabad India
- CSIR‐Central Scientific Instruments Organisation Chandigarh India
| | - Neerja Mittal Garg
- Academy of Scientific and Innovative Research Ghaziabad India
- CSIR‐Central Scientific Instruments Organisation Chandigarh India
| | - S. R. S. Iyengar
- Department of Computer Science and Engineering Indian Institute of Technology Ropar Rupnagar Punjab India
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Sendin K, Manley M, Marini F, Williams PJ. Hierarchical classification pathway for white maize, defect and foreign material classification using spectral imaging. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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14
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Cruz-Tirado J, Fernández Pierna JA, Rogez H, Barbin DF, Baeten V. Authentication of cocoa (Theobroma cacao) bean hybrids by NIR-hyperspectral imaging and chemometrics. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107445] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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15
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Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities. J FOOD QUALITY 2020. [DOI: 10.1155/2020/9474158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Blueberry fruits of different cultivars are featured with different quality indices. In this work, three types of quality factors, including 6 physical parameters, 12 chemical and nutritional components, and 3 antioxidant indices, were measured to compare and classify blueberry fruits from 12 different cultivars in China. Using the autoscaled data of quality factors, unsupervised principal component analysis was performed for exploratory analysis of intercultivar differences and the influences of quality factors. A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. As a result, OVO-PLSDA with 8 quality factors could achieve the classification accuracy of 0.915. This study will provide new insights into the quality variations and key factors among different blueberry cultivars.
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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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Zhang L, Sun H, Rao Z, Ji H. Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 229:117973. [PMID: 31887678 DOI: 10.1016/j.saa.2019.117973] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/11/2019] [Accepted: 12/19/2019] [Indexed: 06/10/2023]
Abstract
In recent years, deep learning models have been widely used in the field of hyperspectral imaging. However, the training of deep learning models requires not only a large number of samples, but also the need to set too many hyper-parameters, which is time consuming and laborious for researchers. This study used hyperspectral imaging technology combined with a deep learning model suitable for small-scale sample data sets, deep forests (DF) model, to classify rice seeds with different degrees of frost damage. During the period, three spectral preprocessing methods (Savitzky-Golay first derivative (SG1), standard normal variate (SNV), and multivariate scatter correction (MSC)) were used to process the original spectral data, and three feature extraction algorithms (principal component analysis (PCA), successive projections algorithm (SPA), and neighborhood component analysis (NCA)) were used to extract the characteristic wavelengths. Moreover, DF model and three traditional machine learning models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were built based on different numbers of sample sets. After multivariate data analysis, it showed that the pretreatment effect of MSC was the most excellent, and the characteristic wavelength extracted by NCA algorithm was the most useful. In addition, the performance of DF model was better than these three traditional classifier models, and it still performed well in small-scale sample set data. Therefore, DF model was chosen as the best classification model. The results of this study show that the DF model maintains good classification performance in small-scale sample set data, and it has a good application prospect in hyperspectral imaging technology.
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Affiliation(s)
- Liu Zhang
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
| | - Heng Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China
| | - Zhenhong Rao
- College of Science, China Agricultural University, Beijing 100083, China
| | - Haiyan Ji
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing 100083, China.
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Krimmer M, Farber C, Kurouski D. Rapid and Noninvasive Typing and Assessment of Nutrient Content of Maize Kernels Using a Handheld Raman Spectrometer. ACS OMEGA 2019; 4:16330-16335. [PMID: 31616810 PMCID: PMC6787905 DOI: 10.1021/acsomega.9b01661] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 09/17/2019] [Indexed: 05/08/2023]
Abstract
To thrive as a global civilization, food production must meet the demands of our ever-growing population. There are more than a billion people on the planet suffering from malnutrition through poor quality or lack of food. Nutrient content of food can be determined by a variety of methods, which have issues such as slow analysis or sample destruction. Near-infrared (NIR) spectroscopy is a long-standing alternative to these methods. In this work, we demonstrated that Raman spectroscopy (RS), another spectroscopic method, can also be used to assess the nutrient content of maize (Zea mays), one of the most widely cultivated grains in the world. Using a handheld Raman spectrometer, we predicted the content of carbohydrates, fibers, carotenoids, and proteins in six different varieties of maize. This analysis requires only a single maize kernel and is fast (1s), portable, noninvasive, and nondestructive. Moreover, we showed that RS in combination with chemometric methods can be used for highly accurate (approximately 90%) spectroscopic typing of maize, which is important for plant breeders and farmers. Finally, we demonstrate that Raman-based approach is as accurate as NIR analysis. These findings suggest that portable Raman systems can be used on combines and grain elevators for autonomous control of grain quality.
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Affiliation(s)
- Mark Krimmer
- Department
of Biochemistry and Biophysics and The Institute for Quantum Science
and Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Charles Farber
- Department
of Biochemistry and Biophysics and The Institute for Quantum Science
and Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Dmitry Kurouski
- Department
of Biochemistry and Biophysics and The Institute for Quantum Science
and Engineering, Texas A&M University, College Station, Texas 77843, United States
- E-mail: . Tel: 979-458-3778
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