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Michelon TB, Carstensen JM, Serra Negra Vieira E, Panobianco M. Multispectral imaging for distinguishing hybrid forest seeds of Corymbia spp. and Eucalyptus spp. from their progenitors. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 363:121383. [PMID: 38843728 DOI: 10.1016/j.jenvman.2024.121383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/27/2024] [Accepted: 06/02/2024] [Indexed: 06/18/2024]
Abstract
In the forest industry, interspecific hybridization, such as Eucalyptus urograndis (Eucalyptus grandis × Eucalyptus urophylla) and Corymbia maculata × Corymbia torelliana, has led to the development of high-performing F1 generations. The successful breeding of these hybrids relies on verifying progenitor origins and confirming post-crossing, but conventional genotype identification methods are resource-intensive and result in seed destruction. As an alternative, multispectral imaging analysis has emerged as an efficient and non-destructive tool for seed phenotyping. This approach has demonstrated success in various crop seeds. However, identifying seed species in the context of forest seeds presents unique challenges due to their natural phenotypic variability and the striking resemblance between different species. This study evaluates the efficacy of spectral imaging analysis in distinguishing hybrid seeds of E. urograndis and C. maculata × C. torelliana from their progenitors. Four experiments were conducted: one for Corymbia spp. seeds, one for each Eucalyptus spp. batch separately, and one for pooled batches. Multispectral images were acquired at 19 wavelengths within the spectral range of 365-970 nm. Classification models based on Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) was created using reflectance and reflectance features, combined with color, shape, and texture features, as well as nCDA transformed features. The LDA algorithm, combining all features, provided the highest accuracy, reaching 98.15% for Corymbia spp., and 92.75%, 85.38, and 86.00 for Eucalyptus batch one, two, and pooled batches, respectively. The study demonstrated the effectiveness of multispectral imaging in distinguishing hybrid seeds of Eucalyptus and Corymbia species. The seeds' spectral signature played a key role in this differentiation. This technology holds great potential for non-invasively classifying forest seeds in breeding programs.
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Affiliation(s)
- Thomas Bruno Michelon
- Department of Plant Science, Federal University of Paraná, R. Dos Funcionários, 1540, CEP 80035-050, Curitiba, PR, Brazil.
| | - Jens Michael Carstensen
- Videometer A/S, Lyngsø Allé 3, DK- 2970, Hørsholm, Denmark; DTU Compute, Technical University of Denmark, DK-2800, Kongens Lyngby, Denmark
| | | | - Maristela Panobianco
- Department of Plant Science, Federal University of Paraná, R. Dos Funcionários, 1540, CEP 80035-050, Curitiba, PR, Brazil
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Tian M, Ma X, Liang M, Zang H. Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion. J AOAC Int 2023; 106:1389-1401. [PMID: 37171863 DOI: 10.1093/jaoacint/qsad058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/25/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species. OBJECTIVE This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR). METHOD Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials. RESULTS For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model. CONCLUSIONS Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods. HIGHLIGHTS This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.
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Affiliation(s)
- Mengyin Tian
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
| | - Xiaobo Ma
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
| | - Mengying Liang
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
| | - Hengchang Zang
- Shandong University, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Cheeloo College of Medicine, Jinan, Shandong 250012, China
- Shandong University, Key Laboratory of Chemical Biology (Ministry of Education), Jinan, Shandong 250012, China
- Shandong University, National Glycoengineering Research Center, Jinan, Shandong 250012, China
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Dutta H, K. M. S, Aski MS, Mishra GP, Sinha SK, Vijay D, C. T. MP, Das S, Pawar PAM, Mishra DC, Singh AK, Kumar A, Tripathi K, Kumar RR, Gupta S, Kumar S, Dikshit HK. Morpho-biochemical characterization of a RIL population for seed parameters and identification of candidate genes regulating seed size trait in lentil ( Lens culinaris Medik.). FRONTIERS IN PLANT SCIENCE 2023; 14:1091432. [PMID: 36875597 PMCID: PMC9975752 DOI: 10.3389/fpls.2023.1091432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
The seed size and shape in lentil (Lens culinaris Medik.) are important quality traits as these influences the milled grain yield, cooking time, and market class of the grains. Linkage analysis was done for seed size in a RIL (F5:6) population derived by crossing L830 (20.9 g/1000 seeds) with L4602 (42.13 g/1000 seeds) which consisted of 188 lines (15.0 to 40.5 g/1000 seeds). Parental polymorphism survey using 394 SSRs identified 31 polymorphic primers, which were used for the bulked segregant analysis (BSA). Marker PBALC449 differentiated the parents and small seed size bulk only, whereas large seeded bulk or the individual plants constituting the large-seeded bulk could not be differentiated. Single plant analysis identified only six recombinant and 13 heterozygotes, of 93 small-seeded RILs (<24.0 g/1000 seed). This clearly showed that the small seed size trait is very strongly regulated by the locus near PBLAC449; whereas, large seed size trait seems governed by more than one locus. The PCR amplified products from the PBLAC449 marker (149bp from L4602 and 131bp from L830) were cloned, sequenced and BLAST searched using the lentil reference genome and was found amplified from chromosome 03. Afterward, the nearby region on chromosome 3 was searched, and a few candidate genes like ubiquitin carboxyl-terminal hydrolase, E3 ubiquitin ligase, TIFY-like protein, and hexosyltransferase having a role in seed size determination were identified. Validation study in another RIL mapping population which is differing for seed size, showed a number of SNPs and InDels among these genes when studied using whole genome resequencing (WGRS) approach. Biochemical parameters like cellulose, lignin, and xylose content showed no significant differences between parents and the extreme RILs, at maturity. Various seed morphological traits like area, length, width, compactness, volume, perimeter, etc., when measured using VideometerLab 4.0 showed significant differences for the parents and RILs. The results have ultimately helped in better understanding the region regulating the seed size trait in genomically less explored crops like lentils.
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Affiliation(s)
- Haragopal Dutta
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Shivaprasad K. M.
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Muraleedhar S. Aski
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Gyan P. Mishra
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Subodh Kumar Sinha
- Indian Council of Agricultural Research (ICAR)-National Institute for Plant Biotechnology, New Delhi, India
| | - Dunna Vijay
- Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi, India
| | - Manjunath Prasad C. T.
- Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi, India
| | - Shouvik Das
- Laboratory of Plant Cell Wall Biology, Regional Centre for Biotechnology, Faridabad, India
| | | | - Dwijesh C. Mishra
- Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kumar Singh
- Division of Genomic Resources, National Bureau of Plant Genetic Resources, New Delhi, India
| | - Atul Kumar
- Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi, India
| | - Kuldeep Tripathi
- Germplasm Evaluation Division, National Bureau of Plant Genetic Resources, New Delhi, India
| | - Ranjeet Ranjan Kumar
- Division of Biochemistry, Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Gupta
- Krishi Bhawan, Indian Council of Agricultural Research, New Delhi, India
| | - Shiv Kumar
- South Asia and China Program, International Center for Agricultural Research in the Dry Areas, National Agriculture Science Complex (NASC) Complex, New Delhi, India
| | - Harsh Kumar Dikshit
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
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4
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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5
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Jia Z, Sun M, Ou C, Sun S, Mao C, Hong L, Wang J, Li M, Jia S, Mao P. Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197521. [PMID: 36236620 PMCID: PMC9572871 DOI: 10.3390/s22197521] [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: 09/06/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 05/24/2023]
Abstract
Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.
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Dutta H, Mishra GP, Aski MS, Bosamia TC, Mishra DC, Bhati J, Sinha SK, Vijay D, C. T. MP, Das S, Pawar PAM, Kumar A, Tripathi K, Kumar RR, Yadava DK, Kumar S, Dikshit HK. Comparative transcriptome analysis, unfolding the pathways regulating the seed-size trait in cultivated lentil (Lens culinaris Medik.). Front Genet 2022; 13:942079. [PMID: 36035144 PMCID: PMC9399355 DOI: 10.3389/fgene.2022.942079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/11/2022] [Indexed: 11/16/2022] Open
Abstract
Market class, cooking time, quality, and milled grain yield are largely influenced by the seed size and shape of the lentil (Lens culinaris Medik.); thus, they are considered to be important quality traits. To unfold the pathways regulating seed size in lentils, a transcriptomic approach was performed using large-seeded (L4602) and small-seeded (L830) genotypes. The study has generated nearly 375 million high-quality reads, of which 98.70% were properly aligned to the reference genome. Among biological replicates, very high similarity in fragments per kilobase of exon per million mapped fragments values (R > 0.9) showed the consistency of RNA-seq results. Various differentially expressed genes associated mainly with the hormone signaling and cell division pathways, transcription factors, kinases, etc. were identified as having a role in cell expansion and seed growth. A total of 106,996 unigenes were used for differential expression (DE) analysis. String analysis identified various modules having certain key proteins like Ser/Thr protein kinase, seed storage protein, DNA-binding protein, microtubule-associated protein, etc. In addition, some growth and cell division–related micro-RNAs like miR3457 (cell wall formation), miR1440 (cell proliferation and cell cycles), and miR1533 (biosynthesis of plant hormones) were identified as having a role in seed size determination. Using RNA-seq data, 5254 EST-SSR primers were generated as a source for future studies aiming for the identification of linked markers. In silico validation using Genevestigator® was done for the Ser/Thr protein kinase, ethylene response factor, and Myb transcription factor genes. It is of interest that the xyloglucan endotransglucosylase gene was found differentially regulated, suggesting their role during seed development; however, at maturity, no significant differences were recorded for various cell wall parameters including cellulose, lignin, and xylose content. This is the first report on lentils that has unfolded the key seed size regulating pathways and unveiled a theoretical way for the development of lentil genotypes having customized seed sizes.
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Affiliation(s)
- Haragopal Dutta
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Gyan P. Mishra
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
- *Correspondence: Gyan P. Mishra, ; Shiv Kumar, ; Harsh Kumar Dikshit,
| | - Muraleedhar S. Aski
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Tejas C. Bosamia
- Plant Omics Division, Central Salt and Marine Chemicals Research Institute, Bhavnagar, India
| | - Dwijesh C. Mishra
- Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Jyotika Bhati
- Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Dunna Vijay
- Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi, India
| | - Manjunath Prasad C. T.
- Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi, India
| | - Shouvik Das
- Laboratory of Plant Cell Wall Biology, Regional Centre for Biotechnology, Faridabad, India
| | | | - Atul Kumar
- Division of Seed Science and Technology, Indian Agricultural Research Institute, New Delhi, India
| | - Kuldeep Tripathi
- Germplasm Evaluation Division, National Bureau of Plant Genetic Resources, New Delhi, India
| | - Ranjeet Ranjan Kumar
- Division of Biochemistry, Indian Agricultural Research Institute, New Delhi, India
| | | | - Shiv Kumar
- South Asia and China Program, International Center for Agricultural Research in the Dry Areas, NASC Complex, New Delhi, India
- *Correspondence: Gyan P. Mishra, ; Shiv Kumar, ; Harsh Kumar Dikshit,
| | - Harsh Kumar Dikshit
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
- *Correspondence: Gyan P. Mishra, ; Shiv Kumar, ; Harsh Kumar Dikshit,
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Mihailova A, Liebisch B, Islam MD, Carstensen JM, Cannavan A, Kelly SD. The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans. Food Chem X 2022; 14:100325. [PMID: 35586030 PMCID: PMC9108882 DOI: 10.1016/j.fochx.2022.100325] [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: 01/17/2022] [Revised: 04/19/2022] [Accepted: 05/03/2022] [Indexed: 11/16/2022] Open
Abstract
Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.
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Affiliation(s)
- Alina Mihailova
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400 Vienna, Austria
| | - Beatrix Liebisch
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400 Vienna, Austria
| | - Marivil D. Islam
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400 Vienna, Austria
| | | | - Andrew Cannavan
- Food Safety and Control Section, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400 Vienna, Austria
| | - Simon D. Kelly
- Food Safety and Control Laboratory, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400 Vienna, Austria
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8
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Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging. J FOOD QUALITY 2021. [DOI: 10.1155/2021/6642220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice.
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Li M, Huang M, Zhu Q, Zhang M, Guo Y, Qin J. Pickled and dried mustard foreign matter detection using multispectral imaging system based on single shot method. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.110106] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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França-Silva F, Rego CHQ, Gomes-Junior FG, de Moraes MHD, de Medeiros AD, da Silva CB. Detection of Drechslera avenae (Eidam) Sharif [ Helminthosporium avenae (Eidam)] in Black Oat Seeds ( Avena strigosa Schreb) Using Multispectral Imaging. SENSORS 2020; 20:s20123343. [PMID: 32545563 PMCID: PMC7348857 DOI: 10.3390/s20123343] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/04/2020] [Accepted: 06/08/2020] [Indexed: 11/16/2022]
Abstract
Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.
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Affiliation(s)
- Fabiano França-Silva
- Department of Crop Science, University of São Paulo-Luiz de Queiroz College of Agriculture, 11 Pádua Dias Avenue, 13418-900 Piracicaba, Brazil; (C.H.Q.R.); (F.G.G.-J.)
- Correspondence:
| | - Carlos Henrique Queiroz Rego
- Department of Crop Science, University of São Paulo-Luiz de Queiroz College of Agriculture, 11 Pádua Dias Avenue, 13418-900 Piracicaba, Brazil; (C.H.Q.R.); (F.G.G.-J.)
| | - Francisco Guilhien Gomes-Junior
- Department of Crop Science, University of São Paulo-Luiz de Queiroz College of Agriculture, 11 Pádua Dias Avenue, 13418-900 Piracicaba, Brazil; (C.H.Q.R.); (F.G.G.-J.)
| | - Maria Heloisa Duarte de Moraes
- Department of Plant Pathology and Nematology, University of São Paulo-Luiz de Queiroz College of Agriculture, 11 Pádua Dias Avenue, Piracicaba 13418-900, Brazil;
| | - André Dantas de Medeiros
- Department of Agronomy, Universidade Federal de Viçosa, Peter Henry Rolfs Avenue, Viçosa MG 36570-900, Brazil;
| | - Clíssia Barboza da Silva
- Laboratory of Radiobiology and Environment, University of São Paulo-Center for Nuclear Energy in Agriculture, 303 Centenário Avenue, Piracicaba SP 13416-000, Brazil;
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11
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Galletti PA, Carvalho MEA, Hirai WY, Brancaglioni VA, Arthur V, Barboza da Silva C. Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato ( Solanum lycopersicum L.) and Carrot ( Daucus carota L.) as Study Cases. FRONTIERS IN PLANT SCIENCE 2020; 11:577851. [PMID: 33408727 PMCID: PMC7779677 DOI: 10.3389/fpls.2020.577851] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/25/2020] [Indexed: 05/22/2023]
Abstract
Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.
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Affiliation(s)
- Patrícia A. Galletti
- Department of Crop Science, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil
| | - Marcia E. A. Carvalho
- Department of Genetics, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil
| | - Welinton Y. Hirai
- Department of Exacts Sciences, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil
| | - Vivian A. Brancaglioni
- Department of Exacts Sciences, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil
| | - Valter Arthur
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Clíssia Barboza da Silva
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
- *Correspondence: Clíssia Barboza da Silva ;
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12
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Classification of Processing Damage in Sugar Beet ( Beta vulgaris) Seeds by Multispectral Image Analysis. SENSORS 2019; 19:s19102360. [PMID: 31121960 PMCID: PMC6566546 DOI: 10.3390/s19102360] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 05/11/2019] [Accepted: 05/16/2019] [Indexed: 11/16/2022]
Abstract
The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.
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13
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ElMasry G, Mandour N, Al-Rejaie S, Belin E, Rousseau D. Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring-An Overview. SENSORS 2019; 19:s19051090. [PMID: 30836613 PMCID: PMC6427362 DOI: 10.3390/s19051090] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 02/17/2019] [Accepted: 02/22/2019] [Indexed: 12/02/2022]
Abstract
As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.
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Affiliation(s)
- Gamal ElMasry
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, Riyadh 11564, Saudi Arabia.
- Faculty of Agriculture, Suez Canal University, Ring Road Km 4.5, Ismailia P.O. Box 41522, Egypt.
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 rue Georges Morel CS 60057, F-49071 Beaucouzé CEDEX, Angers, France.
| | - Nasser Mandour
- Faculty of Agriculture, Suez Canal University, Ring Road Km 4.5, Ismailia P.O. Box 41522, Egypt.
| | - Salim Al-Rejaie
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, Riyadh 11564, Saudi Arabia.
| | - Etienne Belin
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 rue Georges Morel CS 60057, F-49071 Beaucouzé CEDEX, Angers, France.
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France.
| | - David Rousseau
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 42 rue Georges Morel CS 60057, F-49071 Beaucouzé CEDEX, Angers, France.
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France.
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ElMasry G, Mandour N, Wagner MH, Demilly D, Verdier J, Belin E, Rousseau D. Utilization of computer vision and multispectral imaging techniques for classification of cowpea ( Vigna unguiculata) seeds. PLANT METHODS 2019; 15:24. [PMID: 30911323 PMCID: PMC6417027 DOI: 10.1186/s13007-019-0411-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 03/08/2019] [Indexed: 05/22/2023]
Abstract
BACKGROUND The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination. RESULTS The results revealed that the LDA models had good accuracy in distinguishing 'Aged' and 'Non-aged' seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, 'Germinated' and 'Non-germinated' seeds with OCC of 81.80, 79.05 and 81.0%, 'Early germinated', 'Medium germinated' and 'Dead' seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give 'Normal' and 'Abnormal' seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner. CONCLUSION The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality.
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Affiliation(s)
- Gamal ElMasry
- Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, P.O Box 41522, Ismailia, Egypt
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 49071 Beaucouzé, Angers, France
| | - Nasser Mandour
- Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, P.O Box 41522, Ismailia, Egypt
| | - Marie-Hélène Wagner
- GEVES, Station Nationale d’Essais de Semences (SNES), 49071 Beaucouzé, Angers, France
| | - Didier Demilly
- GEVES, Station Nationale d’Essais de Semences (SNES), 49071 Beaucouzé, Angers, France
| | - Jerome Verdier
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 49071 Beaucouzé, Angers, France
| | - Etienne Belin
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Angers, France
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 49071 Beaucouzé, Angers, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Angers, France
- INRA, UMR1345 Institut de Recherche en Horticulture et Semences, 49071 Beaucouzé, Angers, France
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Erazo-Rodas M, Sandoval-Moreno M, Muñoz-Romero S, Huerta M, Rivas-Lalaleo D, Naranjo C, Rojo-Álvarez JL. Multiparametric Monitoring in Equatorian Tomato Greenhouses (I): Wireless Sensor Network Benchmarking. SENSORS (BASEL, SWITZERLAND) 2018; 18:E2555. [PMID: 30081559 PMCID: PMC6111376 DOI: 10.3390/s18082555] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/31/2018] [Accepted: 08/02/2018] [Indexed: 11/17/2022]
Abstract
In recent years, attention has been paid to wireless sensor networks (WSNs) applied to precision agriculture. However, few studies have compared the technologies of different communication standards in terms of topology and energy efficiency. This paper presents the design and implementation of the hardware and software of three WSNs with different technologies and topologies of wireless communication for tomato greenhouses in the Andean region of Ecuador, as well as the comparative study of the performance of each of them. Two companion papers describe the study of the dynamics of the energy consumption and of the monitored variables. Three WSNs were deployed, two of them with the IEEE 802.15.4 standard with star and mesh topologies (ZigBee and DigiMesh, respectively), and a third with the IEEE 802.11 standard with access point topology (WiFi). The measured variables were selected after investigation of the climatic conditions required for efficient tomato growth. The measurements for each variable could be displayed in real time using either a laboratory virtual instrument engineering workbench (LabVIEWTM) interface or an Android mobile application. The comparative study of the three networks made evident that the configuration of the DigiMesh network is the most complex for adding new nodes, due to its mesh topology. However, DigiMesh maintains the bit rate and prevents data loss by the location of the nodes as a function of crop height. It has been also shown that the WiFi network has better stability with larger precision in its measurements.
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Affiliation(s)
- Mayra Erazo-Rodas
- Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador.
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain.
| | - Mary Sandoval-Moreno
- Departamento de Ciencias Exactas, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador.
| | - Sergio Muñoz-Romero
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain.
- Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla del Monte 28660 Madrid, Spain.
| | - Mónica Huerta
- Carrera de Telecomunicaciones, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.
| | - David Rivas-Lalaleo
- Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador.
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain.
| | - César Naranjo
- Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador.
| | - José Luis Rojo-Álvarez
- Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain.
- Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla del Monte 28660 Madrid, Spain.
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16
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Shrestha S, Topbjerg HB, Ytting NK, Skovgård H, Boelt B. Detection of live larvae in cocoons of Bathyplectes curculionis (Hymenoptera: Ichneumonidae) using visible/near-infrared multispectral imaging. PEST MANAGEMENT SCIENCE 2018; 74:2168-2175. [PMID: 29542248 DOI: 10.1002/ps.4915] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 12/31/2017] [Accepted: 03/11/2018] [Indexed: 02/28/2024]
Abstract
BACKGROUND The multispectral (MS) imaging system is a non-destructive method with potential to reduce the labour and time required for quality control in the production of beneficial arthropods such as the parasitoid Bathyplectes curculionis. In Denmark, a project is being undertaken that focuses on the possible use of B. curculionis in augmentative control of Hypera weevil pests in white clover seed production where cocoons of the parasitoid remain as a by-product of seed processing. Only a fraction of the by-product contains live parasitoid larvae and an effective method is required detect live cocoons for later augmentative control of the pest. Therefore, this study aims to identify live larval cocoons of B. curculionis using the MS imaging system. RESULTS Live and dead cocoons were identified using the canonical discriminant analysis (CDA) model with an accuracy of 91% and 80% (error rate 14%) in the training set, and a predicted accuracy of 89% and 81% (error rate 15%) in the test set. Reflectance from the near-infrared region was valuable in identifying live cocoons compared with that from the visible region. CONCLUSION The MS imaging system is a rapid method for the separation of live and dead cocoons of B. curculionis. This study shows the potential of developing an MS imaging system to facilitate sorting of live and dead cocoons and optimize augmentative control of Hypera weevil pests. © 2018 Society of Chemical Industry.
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Affiliation(s)
- Santosh Shrestha
- Department of Agroecology, Science and Technology, Aarhus University, Slagelse, Denmark
| | - Henrik Bak Topbjerg
- Department of Agroecology, Science and Technology, Aarhus University, Slagelse, Denmark
| | - Nanna Karkov Ytting
- Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Skovgård
- Department of Agroecology, Science and Technology, Aarhus University, Slagelse, Denmark
| | - Birte Boelt
- Department of Agroecology, Science and Technology, Aarhus University, Slagelse, Denmark
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17
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Su WH, Sun DW. Multispectral Imaging for Plant Food Quality Analysis and Visualization. Compr Rev Food Sci Food Saf 2018; 17:220-239. [DOI: 10.1111/1541-4337.12317] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, Univ. College Dublin (UCD); National Univ. of Ireland; Belfield Dublin 4 Ireland
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18
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Sendin K, Manley M, Williams PJ. Classification of white maize defects with multispectral imaging. Food Chem 2017; 243:311-318. [PMID: 29146343 DOI: 10.1016/j.foodchem.2017.09.133] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 09/22/2017] [Accepted: 09/26/2017] [Indexed: 11/25/2022]
Abstract
Multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The types of defective materials regarded in grading legislation were divided into 13 classes, and were imaged with a multispectral imaging instrument spanning the UV, visible and NIR regions (19 wavelengths ranging from 375 to 970nm). Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and validated with an independent data set. Results demonstrated good performance in distinguishing between sound maize and undesirable materials, with cross-validated coefficients of determination (Q2) and classification accuracies ranging from 0.35 to 0.99 and 83 to 100%, respectively. Wavelengths related to absorbance of green, yellow and orange colour indicated the presence of lycopene and anthocyanin (505, 525, 570 and 590 nm). NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) were generally identified as important features throughout the study.
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Affiliation(s)
- Kate Sendin
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Marena Manley
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa
| | - Paul J Williams
- Department of Food Science, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch 7602, South Africa.
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Spectral Characterization of a Prototype SFA Camera for Joint Visible and NIR Acquisition. SENSORS 2016; 16:s16070993. [PMID: 27367690 PMCID: PMC4969836 DOI: 10.3390/s16070993] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 06/02/2016] [Accepted: 06/16/2016] [Indexed: 11/17/2022]
Abstract
Multispectral acquisition improves machine vision since it permits capturing more information on object surface properties than color imaging. The concept of spectral filter arrays has been developed recently and allows multispectral single shot acquisition with a compact camera design. Due to filter manufacturing difficulties, there was, up to recently, no system available for a large span of spectrum, i.e., visible and Near Infra-Red acquisition. This article presents the achievement of a prototype of camera that captures seven visible and one near infra-red bands on the same sensor chip. A calibration is proposed to characterize the sensor, and images are captured. Data are provided as supplementary material for further analysis and simulations. This opens a new range of applications in security, robotics, automotive and medical fields.
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20
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Vrešak M, Halkjaer Olesen M, Gislum R, Bavec F, Ravn Jørgensen J. The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification. PLoS One 2016; 11:e0152011. [PMID: 27010656 PMCID: PMC4807013 DOI: 10.1371/journal.pone.0152011] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 03/08/2016] [Indexed: 11/18/2022] Open
Abstract
Application of rapid and time-efficient health diagnostic and identification technology in the seed industry chain could accelerate required analysis, characteristic description and also ultimately availability of new desired varieties. The aim of the study was to evaluate the potential of multispectral imaging and single kernel near-infrared spectroscopy (SKNIR) for determination of seed health and variety separation of winter wheat (Triticum aestivum L.) and winter triticale (Triticosecale Wittm. & Camus). The analysis, carried out in autumn 2013 at AU-Flakkebjerg, Denmark, included nine winter triticale varieties and 27 wheat varieties provided by the Faculty of Agriculture and Life Sciences Maribor, Slovenia. Fusarium sp. and black point disease-infected parts of the seed surface could successfully be distinguished from uninfected parts with use of a multispectral imaging device (405-970 nm wavelengths). SKNIR was applied in this research to differentiate all 36 involved varieties based on spectral differences due to variation in the chemical composition. The study produced an interesting result of successful distinguishing between the infected and uninfected parts of the seed surface. Furthermore, the study was able to distinguish between varieties. Together these components could be used in further studies for the development of a sorting model by combining data from multispectral imaging and SKNIR for identifying disease(s) and varieties.
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Affiliation(s)
- Martina Vrešak
- Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Pivola, Hoce, Slovenia
- * E-mail:
| | - Merete Halkjaer Olesen
- Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark
| | - René Gislum
- Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark
| | - Franc Bavec
- Faculty of Agriculture and Life Sciences, Institute for Organic Farming, University of Maribor, Pivola, Hoce, Slovenia
| | - Johannes Ravn Jørgensen
- Faculty of Science and Technology, Department of Agroecology, Aarhus University, Forsøgsvej, Slagelse, Denmark
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