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Zheng H, Tang W, Yang T, Zhou M, Guo C, Cheng T, Cao W, Zhu Y, Zhang Y, Yao X. Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0200. [PMID: 38978968 PMCID: PMC11227985 DOI: 10.34133/plantphenomics.0200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/18/2024] [Indexed: 07/10/2024]
Abstract
Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF1743, 2), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (R 2 = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the OsmtSSB1L gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.
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Affiliation(s)
- Hengbiao Zheng
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
- Zhongshan Biological Breeding Laboratory,Nanjing, China
| | - Weijie Tang
- Zhongshan Biological Breeding Laboratory,Nanjing, China
- Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology,
Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China
| | - Tao Yang
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Meng Zhou
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Caili Guo
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Tao Cheng
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Weixing Cao
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Yunhui Zhang
- Zhongshan Biological Breeding Laboratory,Nanjing, China
- Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology,
Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China
| | - Xia Yao
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
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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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Mulowayi AM, Shen ZH, Nyimbo WJ, Di ZF, Fallah N, Zheng SH. Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis. Sci Rep 2024; 14:8514. [PMID: 38609452 PMCID: PMC11014857 DOI: 10.1038/s41598-024-59151-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/08/2024] [Indexed: 04/14/2024] Open
Abstract
The study aimed to measure the carotenoid (Car) and pH contents of carrots using hyperspectral imaging. A total of 300 images were collected using a hyperspectral imaging system, covering 472 wavebands from 400 to 1000 nm. Regions of interest (ROIs) were defined to extract average spectra from the hyperspectral images (HIS). We developed two models: least squares support vector machine (LS-SVM) and partial least squares regression (PLSR) to establish a quantitative analysis between the pigment amounts and spectra. The spectra and pigment contents were predicted and correlated using these models. The selection of EWs for modeling was done using the Successive Projections Algorithm (SPA), regression coefficients (RC) from PLSR models, and LS-SVM. The results demonstrated that hyperspectral imaging could effectively evaluate the internal attributes of carrot cortex and xylem. Moreover, these models accurately predicted the Car and pH contents of the carrot parts. This study provides a valuable approach for variable selection and modeling in hyperspectral imaging studies of carrots.
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Affiliation(s)
- Arcel Mutombo Mulowayi
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Zhen Hui Shen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Engineering College, Fujian Jiangxia University, Fuzhou, 350108, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Witness Joseph Nyimbo
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Zhi Feng Di
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China
| | - Nyumah Fallah
- Fujian Provincial Key Laboratory of Agro-Ecological Processing and Safety Monitoring, College of Life Sciences, Agriculture and Forestry University, Fuzhou, 350002, China
| | - Shu He Zheng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
- Fujian University Engineering Research Center for Modern Agricultural Equipment, Fuzhou, 350002, China.
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Ramm R, de Dios Cruz P, Heist S, Kühmstedt P, Notni G. Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry. SENSORS (BASEL, SWITZERLAND) 2024; 24:2290. [PMID: 38610501 PMCID: PMC11014016 DOI: 10.3390/s24072290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024]
Abstract
Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual's health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.
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Affiliation(s)
- Roland Ramm
- Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Str. 7, 07745 Jena, Germany
| | - Pedro de Dios Cruz
- Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Str. 7, 07745 Jena, Germany
| | - Stefan Heist
- Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Str. 7, 07745 Jena, Germany
| | - Peter Kühmstedt
- Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Str. 7, 07745 Jena, Germany
| | - Gunther Notni
- Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Str. 7, 07745 Jena, Germany
- Faculty of Mechanical Engineering, Technical University Ilmenau, Ehrenbergstraße 29, 98693 Ilmenau, Germany
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Mbanjo EGN, Pasion EA, Jones H, Carandang S, Misra G, Ignacio JC, Kretzschmar T, Sreenivasulu N, Boyd LA. Unravelling marker trait associations linking nutritional value with pigmentation in rice seed. THE PLANT GENOME 2023; 16:e20360. [PMID: 37589249 DOI: 10.1002/tpg2.20360] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 05/06/2023] [Accepted: 05/15/2023] [Indexed: 08/18/2023]
Abstract
While considerable breeding effort has focused on increasing the yields of staple crops such as rice and the levels of micronutrients such as iron and zinc, breeding to address the problems of the double-burden of malnutrition has received less attention. Pigmented rice has higher nutritional value and greater health benefits compared to white rice. However, the genetic associations underlying pericarp coloration and accumulation of nutritionally valuable compounds is still poorly understood. Here we report the targeted genetic analysis of 364 rice accessions, assessing the genetic relationship between pericarp coloration (measured using multi-spectral imaging) and a range of phenolic compounds with potential nutritional and health-promoting characteristics. A genome-wide association study resulted in the identification of over 280 single nucleotide polymorphisms (SNPs) associated with the traits of interest. Many of the SNPs were associated with more than one trait, colocalization occurring between nutritional traits, and nutritional and color-related traits. Targeted association analysis identified 67 SNPs, located within 52 candidate genes and associated with 24 traits. Six haplotypes identified within the genes Rc/bHLH17 and OsIPT5 indicated that these genes have an important role in the regulation of a wide range of phenolic compounds, and not only those directly conferring pericarp color. These identified genetic linkages between nutritionally valuable phenolic compounds and pericarp color present not only a valuable resource for the enhancement of the nutritional value of rice but an easy method of selection of suitable genotypes.
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Affiliation(s)
- Edwige Gaby Nkouaya Mbanjo
- International Rice Research Institute (IRRI), Los Baños, Philippines
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Erstelle A Pasion
- International Rice Research Institute (IRRI), Los Baños, Philippines
| | - Huw Jones
- National Institute of Agricultural Botany (NIAB), Cambridge, UK
| | - Socorro Carandang
- International Rice Research Institute (IRRI), Los Baños, Philippines
| | - Gopal Misra
- International Rice Research Institute (IRRI), Los Baños, Philippines
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | | | - Tobias Kretzschmar
- International Rice Research Institute (IRRI), Los Baños, Philippines
- Faculty of Science and Engineering, Southern Cross University, East Lismore, New South Wales, Australia
| | - Nese Sreenivasulu
- International Rice Research Institute (IRRI), Los Baños, Philippines
| | - Lesley Ann Boyd
- National Institute of Agricultural Botany (NIAB), Cambridge, UK
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Qiao J, Liao Y, Yin C, Yang X, Tú HM, Wang W, Liu Y. Vigour testing for the rice seed with computer vision-based techniques. FRONTIERS IN PLANT SCIENCE 2023; 14:1194701. [PMID: 37794935 PMCID: PMC10545894 DOI: 10.3389/fpls.2023.1194701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023]
Abstract
Rice is the staple food for approximately half of the world's population. Seed vigour has a crucial impact on the yield, which can be evaluated by germination rate, vigor index and etc. Existing seed vigour testing methods heavily rely on manual inspections that are destructive, time-consuming, and labor-intensive. To address the drawbacks of existing rice seed vigour testing, we proposed a multispectral image-based non-destructive seed germination testing approach. Specifically, we collected multispectral data in 19 wavebands for six rice varieties. Furthermore, we designed an end-to-end pipeline, denoted as MsiFormer (MisFormer cod3e will be available at https://github.com/LiaoYun0x0/MisFormer) by integrating a Yolo-based object detector (trained Yolo v5) and a vision transformer-based vigour testing model, which effectively improved the automation and efficiency of existing techniques. In order to objectively evaluate the performance of the proposed method in this paper, we conduct a comparison between MisFormer and other 3 deep learning methods. The results showed that, MisFormer performed much better with the accuracy of 94.17%, which was 2.5%-18.34% higher than the other 3 deep learning methods. Besides MsiFormer, possibilities of CIELab mediated image analysis of TTC (tetrazolium chloride) staining in rice seed viability and nCDA (normalized canonical discriminant analysis) in rice seed vigour were also discussed, where CIELab L* of TTC staining were negatively correlated with vigor index and germination rate, with Pearson's correlation coefficient of -0.9874, -0.9802 respectively, and CIELab A* of TTC staining were and positively correlated with vigor index and germination rate, with Pearson's correlation coefficient of 0.9624, 0.9544 respectively, and CIELab A* of nCDA had Pearson's correlation coefficient of -0.8866 and -0.9340 with vigor index and germination rate, respectively. Besides testing methods, vigour results within and among variety(ies) showed that, there were great variations among the 6 rice varieties, and mean coefficient of variation (CV) of vigor index of individual seed within a variety reached 64.87%, revealing the high risk of conventional methods in random sampling. Vigour variations had close relationship with wavelengths of 780 nm-970 nm, indicating their value in future research.
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Affiliation(s)
- Juxiang Qiao
- Quality Standard and Testing Technology Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yun Liao
- Software School, Yunnan University, Kunming, China
| | - Changsheng Yin
- Seed Management Station of Yunnan Province, Kunming, China
| | - Xiaohong Yang
- Quality Standard and Testing Technology Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Hoàng Minh Tú
- National Center for Testing and Testing of Plant Seeds and Products, Hanoi, Vietnam
| | - Wei Wang
- Software School, Yunnan University, Kunming, China
| | - Yanfang Liu
- Quality Standard and Testing Technology Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Aline U, Bhattacharya T, Faqeerzada MA, Kim MS, Baek I, Cho BK. Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review. FRONTIERS IN PLANT SCIENCE 2023; 14:1240361. [PMID: 37662162 PMCID: PMC10471194 DOI: 10.3389/fpls.2023.1240361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.
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Affiliation(s)
- Umuhoza Aline
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | - Tanima Bhattacharya
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
| | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Agricultural Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agricultural Systems, Chungnam National University, Daejeon, Republic of Korea
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Kim SH, Subramanian P, Na YW, Hahn BS, Kim Y. RDA-Genebank and Digital Phenotyping for Next-Generation Research on Plant Genetic Resources. PLANTS (BASEL, SWITZERLAND) 2023; 12:2825. [PMID: 37570979 PMCID: PMC10421229 DOI: 10.3390/plants12152825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023]
Abstract
The National Agrobiodiversity Center under the Rural Development Administration (RDA) in Jeonju, Republic of Korea stands as the foremost national genebank in the country. Over the years, the National Agrobiodiversity Center has remained committed to enriching its collection with foreign genetic resources, elevating its status to a world-class plant genetic resources (PGR)- holding genebank. Currently, several steps are being undertaken to improve the accessibility of the collection to national as well as international researchers, improve the data available on the resources, and amend the passport information for the accessions. With the implementation of the Nagoya Protocol, the origin of genetic resources is being highlighted as an important input in the passport information. The RDA-Genebank actively responds to the Nagoya Protocol by supplementing passport data for resources lacking information on their origin. In addition, a large number of conserved resources are continuously multiplied, and agronomic traits are investigated concurrently. With the traditional methods of characterization of the germplasm requiring a significant amount of time and effort, we have initiated high-throughput phenotyping using digital techniques to improve our germplasm data. Primarily, we have started adding seed phenotype information followed by measuring root phenotypes which are stored under agronomic traits. This may be the initial step toward using largescale high-throughput techniques for a germplasm. In this study, we aim to provide an introduction to the RDA-Genebank, to adopted international standards, and to the establishment of high-throughput phenotyping techniques for the improvement of passport information.
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Affiliation(s)
- Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Parthiban Subramanian
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Young-Wang Na
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Bum-Soo Hahn
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Yoonha Kim
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
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Díaz-Martínez V, Orozco-Sandoval J, Manian V, Dhatt BK, Walia H. A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094370. [PMID: 37177572 PMCID: PMC10181662 DOI: 10.3390/s23094370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.
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Affiliation(s)
| | | | - Vidya Manian
- University of Puerto Rico, Mayagüez, PR 00681, USA
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Hu X, Hu Y, Cai W, Xu Z, Zhao P, Liu X, She Q, Hu Y, Li J. Automatic Detection of Small Sample Apple Surface Defects Using ASDINet. Foods 2023; 12:foods12061352. [PMID: 36981277 PMCID: PMC10048236 DOI: 10.3390/foods12061352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 03/29/2023] Open
Abstract
The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface defect detection under small sample conditions, we designed an apple surface defect detection network (ASDINet) suitable for small sample learning. The self-developed apple sorting system collected RGB images of 50 apple samples for model verification, including non-defective and defective apples (rot, disease, lacerations, and mechanical damage). First, a segmentation network (AU-Net) with a stronger ability to capture small details was designed, and a Dep-conv module that could expand the feature capacity of the receptive field was inserted in its down-sampling path. Among them, the number of convolutional layers in the single-layer convolutional module was positively correlated with the network depth. Next, to achieve real-time segmentation, we replaced the flooding of feature maps with mask output in the 13th layer of the network. Finally, we designed a global decision module (GDM) with global properties, which inserted the global spatial domain attention mechanism (GSAM) and performed fast prediction on abnormal images through the input of masks. In the comparison experiment with state-of-the-art models, our network achieved an AP of 98.8%, and a 97.75% F1-score, which were higher than those of most of the state-of-the-art networks; the detection speed reached 39ms per frame, achieving accuracy-easy deployment and substantial trade-offs that are in line with actual production needs. In the data sensitivity experiment, the ASDINet achieved results that met the production needs under the training of 42 defective pictures. In addition, we also discussed the effect of the ASDINet in actual production, and the test results showed that our proposed network demonstrated excellent performance consistent with the theory in actual production.
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Makmuang S, Terdwongworakul A, Vilaivan T, Maher S, Ekgasit S, Wongravee K. Mapping Hyperspectral NIR Images using Supervised Self-Organizing Maps: Discrimination of Weedy Rice Seeds. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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12
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Zou Z, Chen J, Wu W, Luo J, Long T, Wu Q, Wang Q, Zhen J, Zhao Y, Wang Y, Chen Y, Zhou M, Xu L. Detection of peanut seed vigor based on hyperspectral imaging and chemometrics. FRONTIERS IN PLANT SCIENCE 2023; 14:1127108. [PMID: 36923124 PMCID: PMC10010490 DOI: 10.3389/fpls.2023.1127108] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.
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Affiliation(s)
- Zhiyong Zou
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jie Chen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Weijia Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jinghao Luo
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Tao Long
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qingsong Wu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Qianlong Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Jiangbo Zhen
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongpeng Zhao
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yuchao Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
| | - Yongming Chen
- School of Electrical Engineering and Automation, Hubei Normal University, Huangshi, Hubei, China
| | - Man Zhou
- Food Academy, Sichuan Agricultural University, Yaan, China
| | - Lijia Xu
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Yaan, China
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13
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Sudki JM, Fonseca de Oliveira GR, de Medeiros AD, Mastrangelo T, Arthur V, Amaral da Silva EA, Mastrangelo CB. Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality. FRONTIERS IN PLANT SCIENCE 2023; 14:1112916. [PMID: 36909395 PMCID: PMC9992408 DOI: 10.3389/fpls.2023.1112916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L*) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.
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Affiliation(s)
- Julia Marconato Sudki
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo (CENA/USP), Piracicaba, SP, Brazil
| | - Gustavo Roberto Fonseca de Oliveira
- Department of Crop Science, College of Agricultural Sciences, Faculdade de Ciências Agronômicas (FCA), São Paulo State University (UNESP), Botucati, Brazil
| | | | - Thiago Mastrangelo
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo (CENA/USP), Piracicaba, SP, Brazil
| | - Valter Arthur
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo (CENA/USP), Piracicaba, SP, Brazil
| | - Edvaldo Aparecido Amaral da Silva
- Department of Crop Science, College of Agricultural Sciences, Faculdade de Ciências Agronômicas (FCA), São Paulo State University (UNESP), Botucati, Brazil
| | - Clíssia Barboza Mastrangelo
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo (CENA/USP), Piracicaba, SP, Brazil
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14
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Rice Labeling according to Grain Quality Features Using Laser-Induced Breakdown Spectroscopy. Foods 2023; 12:foods12020365. [PMID: 36673459 PMCID: PMC9858346 DOI: 10.3390/foods12020365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/01/2023] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Rice is an important source of nutrition and energy consumed around the world. Thus, quality inspection is crucial for protecting consumers and increasing the rice's value in the productive chain. Currently, methods for rice labeling depending on grain quality features are based on image and/or visual inspection. These methods have shown subjectivity and inefficiency for large-scale analyses. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique showing attractive features due to how quick the analysis can be carried out and its capability of providing spectra that are true fingerprints of the sample's elemental composition. In this work, LIBS performance was evaluated for labeling rice according to grain quality features. The LIBS spectra of samples with their grain quality numerically described as Type 1, 2, and 3 were measured. Several spectral processing methods were evaluated when modeling a k-nearest neighbors (k-NN) classifier. Variable selection was also carried out by principal component analysis (PCA), and then the optimal k-value was selected. The best result was obtained by applying spectrum smoothing followed by normalization by using the first fifteen principal components (PCs) as input variables and k = 9. Under these conditions, the method showed excellent performance, achieving sample classification with 94% overall prediction accuracy. The sensitivities ranged from 90 to 100%, and specificities were in the range of 92-100%. The proposed method has remarkable characteristics, e.g., analytical speed and analysis guided by chemical responses; therefore, the method is not susceptible to subjectivity errors.
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15
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Jia Z, Ou C, Sun S, Wang J, Liu J, Sun M, Ma W, Li M, Jia S, Mao P. Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity. FRONTIERS IN PLANT SCIENCE 2023; 14:1170947. [PMID: 37152128 PMCID: PMC10157248 DOI: 10.3389/fpls.2023.1170947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023]
Abstract
Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds.
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16
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Xu P, Sun W, Xu K, Zhang Y, Tan Q, Qing Y, Yang R. Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning. Foods 2022; 12:foods12010144. [PMID: 36613360 PMCID: PMC9818215 DOI: 10.3390/foods12010144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.
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Affiliation(s)
- Peng Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Wenbin Sun
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Kang Xu
- College of Information and Communication Engineering, Hainan University, Haikou 570228, China
| | - Yunpeng Zhang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Qian Tan
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Yiren Qing
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
| | - Ranbing Yang
- College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
- Correspondence: ; Tel.: +86-0898-66267576
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17
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Månefjord H, Li M, Brackmann C, Reistad N, Runemark A, Rota J, Anderson B, Zoueu JT, Merdasa A, Brydegaard M. A biophotonic platform for quantitative analysis in the spatial, spectral, polarimetric, and goniometric domains. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2022; 93:113709. [PMID: 36461456 DOI: 10.1063/5.0095133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/21/2022] [Indexed: 06/17/2023]
Abstract
Advanced instrumentation and versatile setups are needed for understanding light interaction with biological targets. Such instruments include (1) microscopes and 3D scanners for detailed spatial analysis, (2) spectral instruments for deducing molecular composition, (3) polarimeters for assessing structural properties, and (4) goniometers probing the scattering phase function of, e.g., tissue slabs. While a large selection of commercial biophotonic instruments and laboratory equipment are available, they are often bulky and expensive. Therefore, they remain inaccessible for secondary education, hobbyists, and research groups in low-income countries. This lack of equipment impedes hands-on proficiency with basic biophotonic principles and the ability to solve local problems with applied physics. We have designed, prototyped, and evaluated the low-cost Biophotonics, Imaging, Optical, Spectral, Polarimetric, Angular, and Compact Equipment (BIOSPACE) for high-quality quantitative analysis. BIOSPACE uses multiplexed light-emitting diodes with emission wavelengths from ultraviolet to near-infrared, captured by a synchronized camera. The angles of the light source, the target, and the polarization filters are automated by low-cost mechanics and a microcomputer. This enables multi-dimensional scatter analysis of centimeter-sized biological targets. We present the construction, calibration, and evaluation of BIOSPACE. The diverse functions of BIOSPACE include small animal spectral imaging, measuring the nanometer thickness of a bark-beetle wing, acquiring the scattering phase function of a blood smear and estimating the anisotropic scattering and the extinction coefficients, and contrasting muscle fibers using polarization. We provide blueprints, component list, and software for replication by enthusiasts and educators to simplify the hands-on investigation of fundamental optical properties in biological samples.
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Affiliation(s)
- Hampus Månefjord
- Department of Physics, Lund University, Sölvegatan 14, SE-223 62 Lund, Sweden
| | - Meng Li
- Department of Physics, Lund University, Sölvegatan 14, SE-223 62 Lund, Sweden
| | - Christian Brackmann
- Department of Physics, Lund University, Sölvegatan 14, SE-223 62 Lund, Sweden
| | - Nina Reistad
- Department of Physics, Lund University, Sölvegatan 14, SE-223 62 Lund, Sweden
| | - Anna Runemark
- Department of Biology, Lund University, Sölvegatan 35, SE-223 63 Lund, Sweden
| | - Jadranka Rota
- Biological Museum, Department of Biology, Lund University, Sölvegatan 37, SE-223 62 Lund, Sweden
| | | | - Jeremie T Zoueu
- Laboratoire d'Instrumentation, Image et Spectroscopie, INP-HB, BP 1093 Yamoussoukro, Côte d'Ivoire
| | - Aboma Merdasa
- Department of Physics, Lund University, Sölvegatan 14, SE-223 62 Lund, Sweden
| | - Mikkel Brydegaard
- Department of Physics, Lund University, Sölvegatan 14, SE-223 62 Lund, Sweden
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18
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Wang F, Yi Q, Xie L, Yao X, Zheng J, Xu T, Li J, Chen S. Non-destructive monitoring of amylose content in rice by UAV-based hyperspectral images. FRONTIERS IN PLANT SCIENCE 2022; 13:1035379. [PMID: 36388531 PMCID: PMC9647158 DOI: 10.3389/fpls.2022.1035379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Amylose content (AC) is an important indicator for rice quality grading. The rapid development of unmanned aerial vehicle (UAV) technology provides rich spectral and spatial information on observed objects, making non-destructive monitoring of crop quality possible. To test the potential of UAV-based hyperspectral images in AC estimation, in this study, observations on five rice cultivars were carried out in eastern China (Zhejiang province) for four consecutive years (from 2017 to 2020). The correlations between spectral and textural variables of UAV-based hyperspectral images at different growth stages (booting, heading, filling, and ripening) and AC (%) were analyzed, and the linear regression models based on spectral variables alone, textural variables alone, and combined spectral and textural variables were established. The results showed that the sensitive bands (P< 0.001) to AC were mainly centered in the green (536∽568 nm) and red regions (630∽660nm), with spectral and textural variables at the ripening stage giving the highest negative correlation coefficient of -0.868 and -0.824, respectively. Models based on combined spectral and textural variables give better estimation than those based on spectral or textural variables alone, characterized by less variables and higher accuracy. The best models using spectral or textural variables alone both involved three growth stages (heading, filling, and ripening), with root mean square error (RMSE) of 1.01% and 1.04%, respectively, while the models based on combined spectral and textural variables have RMSE of 1.04% 0.844% with only one (ripening stage) or two (ripening and filling stages) growth stages involved. The combination of spectral and textural variables of UAV-based hyperspectral images is expected to simplify data acquisition and enhance estimation accuracy in remote sensing of rice AC.
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Affiliation(s)
- Fumin Wang
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
- Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou, China
| | - Qiuxiang Yi
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
| | - Lili Xie
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
| | - Xiaoping Yao
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
| | - Jueyi Zheng
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
| | - Tianyue Xu
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
| | - Jiale Li
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
| | - Siting Chen
- Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China
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19
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Chandran AKN, Sandhu J, Irvin L, Paul P, Dhatt BK, Hussain W, Gao T, Staswick P, Yu H, Morota G, Walia H. Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress. FRONTIERS IN PLANT SCIENCE 2022; 13:1026472. [PMID: 36304400 PMCID: PMC9593041 DOI: 10.3389/fpls.2022.1026472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress.
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Affiliation(s)
| | - Jaspreet Sandhu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Larissa Irvin
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Puneet Paul
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Balpreet K. Dhatt
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Banos, Philippines
| | - Tian Gao
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Paul Staswick
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Hongfeng Yu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
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20
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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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21
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An ultra-small nine-color spectrometer with a two-layer biparted ten-dichroic-mirror array and an image sensor. Sci Rep 2022; 12:16518. [PMID: 36192470 PMCID: PMC9529936 DOI: 10.1038/s41598-022-20814-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/19/2022] [Indexed: 11/08/2022] Open
Abstract
An ultra-small (54 × 58 × 8.5 mm) and large aperture (1 × 7 mm) nine-color spectrometer-using an array of ten dichroic mirrors "biparted" as two layers-was developed and used for snapshot spectral imaging. Incident-light flux with a cross section smaller than the aperture size is split into nine color fluxes with 20-nm-width contiguous wavelength bands and central wavelengths of 530, 550, 570, 590, 610, 630, 650, 670, and 690 nm. Images of the nine color fluxes are simultaneously and efficiently measured by an image sensor. Unlike a conventional dichroic-mirror array, the developed dichroic-mirror array has a unique biparted configuration that not only increases the number of colors that can be measured simultaneously but also improves the image resolution of each color flux. The developed nine-color spectrometer was used for four-capillary-array electrophoresis. Eight dyes concurrently migrating in each capillary were simultaneously quantified by nine-color laser-induced fluorescence detection. Since the nine-color spectrometer is not only ultra-small and inexpensive but also has high light throughput and sufficient spectral resolution for most spectral-imaging applications, it has the potential to be widely used in various fields.
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22
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Buenafe RJ, Tiozon R, Boyd LA, Sartagoda KJ, Sreenivasulu N. Mathematical modeling to predict rice's phenolic and mineral content through multispectral imaging. FOOD CHEMISTRY ADVANCES 2022; 1:None. [PMID: 36570628 PMCID: PMC9767410 DOI: 10.1016/j.focha.2022.100141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/29/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022]
Abstract
Over half the world population relies on rice for energy, but being a carbohydrate-based crop, it offers limited nutritional benefits. To achieve nutritional security targets in Asia, we must understand the genetic variation in multi-nutritional properties with therapeutic properties and deploy this knowledge to future rice breeding. High throughput, VideometerLAB spectral imaging data has been effective in estimating total anthocyanin content, particularly bound anthocyanin content, using the high prediction power of partial least square (PLS) regression models. Multi-pronged nutritional properties of phenolic compounds and minerals, together with videometerLAB features, were utilized to develop models to classify a collection of black rice varieties into three distinct nutritional quality ideotypes. These derived models for black rice diversity panels were created utilizing videometerLAB data (L, A, B parameters), selected phenolic types (total phenolics, total anthocyanins, and bound flavonoids), and minerals (Molybdenum and Phosphorous). Random forest and artificial neural network models depicted the multi-nutritional features of black rice with 85.35 and 99.9% accuracy, respectively. These prediction algorithms would help rice breeders strategically breed nutritionally valuable genotypes based on simple, high-through-put videometerLAB readings and a small number of nutritional assays.
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Affiliation(s)
- Reuben James Buenafe
- Consumer-driven Grain Quality and Nutrition Unit, Rice Breeding and Innovations Cluster, International Rice Research Institute, Los Baños, Philippines
| | - Rhowell Tiozon
- Consumer-driven Grain Quality and Nutrition Unit, Rice Breeding and Innovations Cluster, International Rice Research Institute, Los Baños, Philippines,Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | | | - Kristel June Sartagoda
- Consumer-driven Grain Quality and Nutrition Unit, Rice Breeding and Innovations Cluster, International Rice Research Institute, Los Baños, Philippines
| | - Nese Sreenivasulu
- Consumer-driven Grain Quality and Nutrition Unit, Rice Breeding and Innovations Cluster, International Rice Research Institute, Los Baños, Philippines,Corresponding author.
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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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24
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Wang Z, Huang W, Tian X, Long Y, Li L, Fan S. Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods. FRONTIERS IN PLANT SCIENCE 2022; 13:849495. [PMID: 35620676 PMCID: PMC9127793 DOI: 10.3389/fpls.2022.849495] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/05/2022] [Indexed: 06/15/2023]
Abstract
The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000-2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.
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Affiliation(s)
- Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Yuan Long
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Lianjie Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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25
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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: 8] [Impact Index Per Article: 4.0] [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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26
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Zhang S, Zeng H, Ji W, Yi K, Yang S, Mao P, Wang Z, Yu H, Li M. Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology. SENSORS 2022; 22:s22072760. [PMID: 35408374 PMCID: PMC9003024 DOI: 10.3390/s22072760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/24/2022] [Accepted: 03/31/2022] [Indexed: 02/01/2023]
Abstract
Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.
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Affiliation(s)
- Shuheng Zhang
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
| | - Hanguo Zeng
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
| | - Wei Ji
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
| | - Kun Yi
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
| | - Shuangfeng Yang
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
| | - Peisheng Mao
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
| | - Zhanjun Wang
- Institute of Desertification Control, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China; (Z.W.); (H.Y.)
| | - Hongqian Yu
- Institute of Desertification Control, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China; (Z.W.); (H.Y.)
| | - Manli Li
- College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China; (S.Z.); (H.Z.); (W.J.); (K.Y.); (S.Y.); (P.M.)
- Correspondence:
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27
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Bartolić D, Mutavdžić D, Carstensen JM, Stanković S, Nikolić M, Krstović S, Radotić K. Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B 1 contaminated (Zea mays L.) seeds: a preliminary study. Sci Rep 2022; 12:4849. [PMID: 35318372 PMCID: PMC8940939 DOI: 10.1038/s41598-022-08352-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 03/04/2022] [Indexed: 12/04/2022] Open
Abstract
Cereal seeds safety may be compromised by the presence of toxic contaminants, such as aflatoxins. Besides being carcinogenic, they have other adverse health effects on humans and animals. In this preliminary study, we used two non-invasive optical techniques, optical fiber fluorescence spectroscopy and multispectral imaging (MSI), for discrimination of maize seeds naturally contaminated with aflatoxin B1 (AFB1) from the uncontaminated seeds. The AFB1-contaminated seeds exhibited a red shift of the emission maximum position compared to the control samples. Using linear discrimination analysis to analyse fluorescence data, classification accuracy of 100% was obtained to discriminate uncontaminated and AFB1-contaminated seeds. The MSI analysis combined with a normalized canonical discriminant analysis, provided spectral and spatial patterns of the analysed seeds. The AFB1-contaminated seeds showed a 7.9 to 9.6-fold increase in the seed reflectance in the VIS region, and 10.4 and 12.2-fold increase in the NIR spectral region, compared with the uncontaminated seeds. Thus the MSI method classified successfully contaminated from uncontaminated seeds with high accuracy. The results may have an impact on development of spectroscopic non-invasive methods for detection of AFs presence in seeds, providing valuable information for the assessment of seed adulteration in the field of food forensics and food safety.
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Affiliation(s)
- Dragana Bartolić
- University of Belgrade, Institute for Multidisciplinary Research, P.O. Box 33, 11030, Belgrade, Serbia
| | - Dragosav Mutavdžić
- University of Belgrade, Institute for Multidisciplinary Research, P.O. Box 33, 11030, Belgrade, Serbia
| | | | - Slavica Stanković
- Maize Research Institute, Zemun Polje, Slobodana Bajića 1, 11185, Belgrade, Serbia
| | - Milica Nikolić
- Maize Research Institute, Zemun Polje, Slobodana Bajića 1, 11185, Belgrade, Serbia
| | - Saša Krstović
- Department of Animal Science, Faculty of Agriculture, University of Novi Sad, Novi Sad, Serbia
| | - Ksenija Radotić
- University of Belgrade, Institute for Multidisciplinary Research, P.O. Box 33, 11030, Belgrade, Serbia.
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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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29
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Xu X, Luo XQ, Zhang J, Zhu W, Chen Z, Li TF, Liu WM, Wang XL. Near-infrared plasmonic sensing and digital metasurface via double Fano resonances. OPTICS EXPRESS 2022; 30:5879-5895. [PMID: 35209541 DOI: 10.1364/oe.452134] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
Plasmonic sensing that enables the detection of minute events, when the incident light field interacts with the nanostructure interface, has been widely applied to optical and biological detection. Implementation of the controllable plasmonic double Fano resonances (DFRs) offers a flexible and efficient way for plasmonic sensing. However, plasmonic sensing and digital metasurface induced by tailorable plasmonic DFRs require further study. In this work, we numerically and theoretically investigate the near-infrared plasmonic DFRs for plasmonic sensing and digital metasurface in a hybrid metasurface with concentric ϕ-shaped-hole and circular-ring-aperture unit cells. We show that a plasmonic Fano resonance, resulting from the interaction between a narrow and a wide effective dipolar modes, can be realized in the ϕ-shaped hybrid metasurface. In particular, we demonstrate that the tailoring plasmonic DFRs with distinct mechanisms of actions can be accomplished in three different ϕ-shaped hybrid metasurfaces. Moreover, the resonance mode-broadening and mode-shifting plasmonic sensing can be fulfilled by modulating the polarization orientation and the related geometric parameters of the unit cells in the near-infrared waveband, respectively. In addition, the plasmonic switch with a high ON/OFF ratio can not only be achieved but also be exploited to establish a single-bit digital metasurface, even empower to implement two- and three-bit digital metasurface characterized by the plasmonic DFRs in the telecom L-band. Our results offer a new perspective toward realizing polarization-sensitive optical sensing, passive optical switches, and programmable metasurface devices, which also broaden the landscape of subwavelength nanostructures for biosensors and optical communications.
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Wang C, Caragea D, Kodadinne Narayana N, Hein NT, Bheemanahalli R, Somayanda IM, Jagadish SVK. Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature. PLANT METHODS 2022; 18:9. [PMID: 35065667 PMCID: PMC8783510 DOI: 10.1186/s13007-022-00839-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 01/06/2022] [Indexed: 05/02/2023]
Abstract
BACKGROUND Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. RESULTS We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. CONCLUSIONS We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.
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Affiliation(s)
- Chaoxin Wang
- Department of Computer Science, Kansas State University, Manhattan, KS 66506 USA
| | - Doina Caragea
- Department of Computer Science, Kansas State University, Manhattan, KS 66506 USA
| | - Nisarga Kodadinne Narayana
- Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Mississippi State, MS 39762 USA
| | - Nathan T. Hein
- Department of Agronomy, Kansas State University, Manhattan, KS 66506 USA
| | - Raju Bheemanahalli
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762 USA
| | - Impa M. Somayanda
- Department of Agronomy, Kansas State University, Manhattan, KS 66506 USA
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Fonseca de Oliveira GR, Mastrangelo CB, Hirai WY, Batista TB, Sudki JM, Petronilio ACP, Crusciol CAC, Amaral da Silva EA. An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality. FRONTIERS IN PLANT SCIENCE 2022; 13:849986. [PMID: 35498679 PMCID: PMC9048030 DOI: 10.3389/fpls.2022.849986] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/21/2022] [Indexed: 05/05/2023]
Abstract
Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F0, Fm, and Fv/Fm) and stress indices (anthocyanin and NDVI). Artificial intelligence features (QDA method) applied to the data extracted from the seed images categorized lots with high and low quality. Higher levels of anthocyanin were found in the leaves of seedlings from low quality seeds. Therefore, this information is promising since the initial behavior of the seedlings reflected the quality of the seeds. The existence of new markers that effectively screen peanut seed quality was confirmed. The combination of physical properties (area, length, width, and coat brightness), pigments (chlorophyll fluorescence and anthocyanin), and light reflectance (660, 690, and 780 nm), is highly efficient to identify peanut seedlots with superior quality (98% accuracy).
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Affiliation(s)
- Gustavo Roberto Fonseca de Oliveira
- Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil
- *Correspondence: Gustavo Roberto Fonseca de Oliveira,
| | - Clíssia Barboza Mastrangelo
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Welinton Yoshio Hirai
- Department of Exacts Sciences, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil
| | - Thiago Barbosa Batista
- Department of Crop Science, College of Agricultural Sciences, São Paulo State University, Botucatu, Brazil
| | - Julia Marconato Sudki
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
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Aletti G, Benfenati A, Naldi G. A Semi-Supervised Reduced-Space Method for Hyperspectral Imaging Segmentation. J Imaging 2021; 7:jimaging7120267. [PMID: 34940734 PMCID: PMC8706750 DOI: 10.3390/jimaging7120267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/26/2021] [Accepted: 12/01/2021] [Indexed: 11/19/2022] Open
Abstract
The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving many scientific and practical problems in agriculture, biomedical, ecological, geological, hydrological studies. However, their analysis requires developing specialized and fast algorithms for data processing, due the high dimensionality of the data. In this work, we propose a new semi-supervised method for multilabel segmentation of HSI that combines a suitable linear discriminant analysis, a similarity index to compare different spectra, and a random walk based model with a direct label assignment. The user-marked regions are used for the projection of the original high-dimensional feature space to a lower dimensional space, such that the class separation is maximized. This allows to retain in an automatic way the most informative features, lightening the successive computational burden. The part of the random walk is related to a combinatorial Dirichlet problem involving a weighted graph, where the nodes are the projected pixel of the original HSI, and the positive weights depend on the distances between these nodes. We then assign to each pixel of the original image a probability quantifying the likelihood that the pixel (node) belongs to some subregion. The computation of the spectral distance involves both the coordinates in a features space of a pixel and of its neighbors. The final segmentation process is therefore reduced to a suitable optimization problem coupling the probabilities from the random walker computation, and the similarity with respect the initially labeled pixels. We discuss the properties of the new method with experimental results carried on benchmark images.
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33
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Lensless Multispectral Camera Based on a Coded Aperture Array. SENSORS 2021; 21:s21227757. [PMID: 34833833 PMCID: PMC8620651 DOI: 10.3390/s21227757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/19/2021] [Accepted: 11/20/2021] [Indexed: 11/16/2022]
Abstract
Multispectral imaging can be applied to water quality monitoring, medical diagnosis, and other applications, but the principle of multispectral imaging is different from the principle of hyper-spectral imaging. Multispectral imaging is generally achieved through filters, so multiple photos are required to obtain spectral information. Using multiple detectors to take pictures at the same time increases the complexity and cost of the system. This paper proposes a simple multispectral camera based on lensless imaging, which does not require multiple lenses. The core of the system is the multispectral coding aperture. The coding aperture is divided into different regions and each region transmits the light of one wavelength, such that the spectral information of the target can be coded. By solving the inverse problem of sparse constraints, the multispectral information of the target is inverted. Herein, we analyzed the characteristics of this multispectral camera and developed a principle prototype to obtain experimental results.
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Nehoshtan Y, Carmon E, Yaniv O, Ayal S, Rotem O. Robust seed germination prediction using deep learning and RGB image data. Sci Rep 2021; 11:22030. [PMID: 34764422 PMCID: PMC8586350 DOI: 10.1038/s41598-021-01712-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 11/02/2021] [Indexed: 11/19/2022] Open
Abstract
Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with wasteful disqualification of seed lots holding considerable amounts of good seeds and further translates to financial losses and supply chain insecurity. Here, we present the first-ever generic germination prediction technology that is based on deep learning and RGB image data and facilitates seed classification by seed germinability and usability, two facets of germination fate. We show technology competence to render dozens of disqualified seed lots of seven vegetable crops, representing different genetics and production pipelines, industrially appropriate, and to adequately classify lots by utilizing available crop-level image data, instead of lot-specific data. These achievements constitute a major milestone in the deployment of this technology for industrial seed sorting by germination fate for multiple crops.
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Affiliation(s)
| | | | | | | | - Or Rotem
- Seed-X LTD, 5691000, Magshimim, Israel.
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Engels JMM, Ebert AW. A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. II. Strengths and Weaknesses of the Current System and Recommendations for Its Improvement. PLANTS (BASEL, SWITZERLAND) 2021; 10:1904. [PMID: 34579439 PMCID: PMC8472064 DOI: 10.3390/plants10091904] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/05/2021] [Accepted: 09/09/2021] [Indexed: 02/08/2023]
Abstract
In this paper, we review gene bank operations that have an influence on the global conservation system, with the intention to identify critical aspects that should be improved for optimum performance. We describe the role of active and base collections and the importance of linking germplasm conservation and use, also in view of new developments in genomics and phenomics that facilitate more effective and efficient conservation and use of plant agrobiodiversity. Strengths, limitations, and opportunities of the existing global ex situ conservation system are discussed, and measures are proposed to achieve a rational, more effective, and efficient global system for germplasm conservation and sustainable use. The proposed measures include filling genetic and geographic gaps in current ex situ collections; determining unique accessions at the global level for long-term conservation in virtual base collections; intensifying existing international collaborations among gene banks and forging collaborations with the botanic gardens community; increasing investment in conservation research and user-oriented supportive research; improved accession-level description of the genetic diversity of crop collections; improvements of the legal and policy framework; and oversight of the proposed network of global base collections.
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Barboza da Silva C, Oliveira NM, de Carvalho MEA, de Medeiros AD, de Lima Nogueira M, Dos Reis AR. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality. Sci Rep 2021; 11:17834. [PMID: 34497292 PMCID: PMC8426380 DOI: 10.1038/s41598-021-97223-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/13/2021] [Indexed: 12/03/2022] Open
Abstract
In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.
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Affiliation(s)
- Clíssia Barboza da Silva
- Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba, SP, 13416-000, Brazil.
| | - Nielsen Moreira Oliveira
- Department of Crop Science, College of Agriculture Luiz de Queiroz (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil
| | - Marcia Eugenia Amaral de Carvalho
- Department of Genetics, College of Agriculture Luiz de Queiroz (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil
| | | | - Marina de Lima Nogueira
- Department of Genetics, College of Agriculture Luiz de Queiroz (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil
| | - André Rodrigues Dos Reis
- Department of Biosystems Engineering, School of Sciences and Engineering, São Paulo State University (UNESP), Tupã, SP, 17602-496, Brazil
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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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Wang X, Zhang H, Song R, He X, Mao P, Jia S. Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis. SENSORS 2021; 21:s21175804. [PMID: 34502695 PMCID: PMC8434479 DOI: 10.3390/s21175804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/27/2021] [Accepted: 08/26/2021] [Indexed: 11/16/2022]
Abstract
Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.
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Li H, Zhang L, Sun H, Rao Z, Ji H. Identification of soybean varieties based on hyperspectral imaging technology and one‐dimensional convolutional neural network. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13767] [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)
- Hao Li
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education China Agricultural University Beijing China
- Key Laboratory of Agricultural 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 Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing China
| | - Heng Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education China Agricultural University Beijing China
- Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing 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 Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing China
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Davis BI, Agraz CB, Kline M, Gottschall E, Nolt M, Whitaker TB, Osborne JA, Tengstrand E, Ostrowski K, Teixeira R, Davis JP. Measurements of High Oleic Purity in Peanut Lots Using Rapid, Single Kernel Near‐Infrared Reflectance Spectroscopy. J AM OIL CHEM SOC 2021. [DOI: 10.1002/aocs.12487] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Brittany I. Davis
- Technical Services JLA International Albany GA 31721 USA
- Technical Services IEH Laboratories Lake Forest Park WA 98155 USA
| | - Catherine B. Agraz
- Technical Services JLA International Albany GA 31721 USA
- Technical Services IEH Laboratories Lake Forest Park WA 98155 USA
| | | | | | - Michael Nolt
- Research and Development, Hershey Hershey 17033 USA
| | - Thomas B. Whitaker
- Biological and Agricultural Engineering Department, Statistics Department Food, Bioprocessing and Nutrition Sciences Department North Carolina State University Raleigh 27695 USA
| | - Jason A. Osborne
- Statistics Department North Carolina State University Raleigh 27695 USA
| | - Erik Tengstrand
- Research and Development Division QualySense AG Zürich CH‐8152 Switzerland
| | - Kamil Ostrowski
- Research and Development Division QualySense AG Zürich CH‐8152 Switzerland
| | - Rita Teixeira
- Biological and Agricultural Engineering Department, Statistics Department Food, Bioprocessing and Nutrition Sciences Department North Carolina State University Raleigh 27695 USA
| | - Jack P. Davis
- Technical Services JLA International Albany GA 31721 USA
- Technical Services IEH Laboratories Lake Forest Park WA 98155 USA
- Food, Bioprocessing and Nutrition Sciences Department, North Carolina State University Raleigh 27695 USA
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Yao L, van de Zedde R, Kowalchuk G. Recent developments and potential of robotics in plant eco-phenotyping. Emerg Top Life Sci 2021; 5:289-300. [PMID: 34013965 PMCID: PMC8166337 DOI: 10.1042/etls20200275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 02/04/2023]
Abstract
Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.
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Affiliation(s)
- Lili Yao
- Wageningen University & Research, Wageningen, Netherlands
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Komyshev EG, Genaev MA, Afonnikov DA. Analysis of color and texture characteristics of cereals on digital images. Vavilovskii Zhurnal Genet Selektsii 2021; 24:340-347. [PMID: 33659816 PMCID: PMC7716559 DOI: 10.18699/vj20.626] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The color of the grain shell of cereals is an important feature that characterizes the pigments and
metabolites contained in it. The grain shell is the main barrier between the grain and the environment, so its
characteristics are associated with a number of important biological functions: moisture absorption, grain viability, resistance to pre-harvest germination. The presence of pigments in the shell affects various technological properties of the grain. Color characteristics, as well as the appearance of the grain shell are an important
indicator of plant diseases. In addition, the color of the grains serves as a classifying feature of plants. Genetic
control of the color formation of both grains and other plant organs is exerted by genes encoding enzymes
involved in the biosynthesis of pigments, as well as regulatory genes. For a number of pigments, these genes
are well understood, but for some pigments, such as melanin, which causes the black color of grains in barley, the molecular mechanisms of biosynthesis are still poorly understood. When studying the mechanisms of
genetic control of grain color, breeders and geneticists are constantly faced with the need to assess the color
characteristics of their shell. The technical means of addressing this problem include spectrophotometers,
spectrometers, hyperspectral cameras. However, these cameras are expensive, especially with high resolution,
both spatial and spectral. An alternative is to use digital cameras that allow you to get high-quality images
with high spatial and color resolution. In this regard, recently, in the field of plant phenotyping, methods for
evaluating the color and texture characteristics of cereals based on the analysis of two-dimensional images
obtained by digital cameras have been intensively developed. This mini-review is devoted to the main tasks
related to the analysis of color and texture characteristics of cereals, and to methods of their description based
on digital images.
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Affiliation(s)
- E G Komyshev
- Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Kurchatov Genomic Center of the Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - M A Genaev
- Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Kurchatov Genomic Center of the Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
| | - D A Afonnikov
- Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Kurchatov Genomic Center of the Institute of Cytology and Genetics of Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia
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Antequera T, Caballero D, Grassi S, Uttaro B, Perez-Palacios T. Evaluation of fresh meat quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A review. Meat Sci 2021; 172:108340. [DOI: 10.1016/j.meatsci.2020.108340] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/23/2020] [Accepted: 10/09/2020] [Indexed: 12/31/2022]
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Bianchini VDJM, Mascarin GM, Silva LCAS, Arthur V, Carstensen JM, Boelt B, Barboza da Silva C. Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality. PLANT METHODS 2021; 17:9. [PMID: 33499879 PMCID: PMC7836195 DOI: 10.1186/s13007-021-00709-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 01/16/2021] [Indexed: 05/22/2023]
Abstract
BACKGROUND The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. RESULTS We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. CONCLUSIONS Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.
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Affiliation(s)
- Vitor de Jesus Martins Bianchini
- Department of Crop Science, College of Agriculture "Luiz de Queiroz", University of São Paulo, 11 Padua Dias Ave, Box 9, Piracicaba, SP, 13418-900, Brazil
| | - Gabriel Moura Mascarin
- Laboratory of Environmental Microbiology, Brazilian Agricultural Research Corporation, Embrapa Environment, Rodovia SP 340, Km 127.5, Jaguariúna, 13820-000, Brazil
| | - Lúcia Cristina Aparecida Santos Silva
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, 303 Centenario Ave., Sao Dimas, Piracicaba, SP, 13416-000, Brazil
| | - Valter Arthur
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, 303 Centenario Ave., Sao Dimas, Piracicaba, SP, 13416-000, Brazil
| | | | - Birte Boelt
- Department of Agroecology, Science and Technology, Aarhus University, 4200, Slagelse, Denmark
| | - Clíssia Barboza da Silva
- Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, 303 Centenario Ave., Sao Dimas, Piracicaba, SP, 13416-000, Brazil.
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Yu K, Fang S, Zhao Y. Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118917. [PMID: 32949945 DOI: 10.1016/j.saa.2020.118917] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 08/16/2020] [Accepted: 09/01/2020] [Indexed: 06/11/2023]
Abstract
Accurate detection of heavy metal stress on the growth status of plants is of great concern for agricultural production and management, food security, and ecological environment. A proximal hyperspectral imaging (HSI) system covered the visible/near-infrared (Vis/NIR) region of 400-1000 nm coupled with machine learning methods were employed to discriminate the tobacco plants stressed by different concentration of heavy metal Hg. After acquiring hyperspectral images of tobacco plants stressed by heavy metal Hg with concentration solutions of 0 mg·L-1 (non-stressed groups), 1, 3, and 5 mg·L-1 (3 stressed groups), regions of interest (ROIs) of canopy in tobacco plants were identified for spectra processing. Meanwhile, tobacco plant's appearance and microstructure of mesophyll tissue in tobacco leaves were analyzed. After that, clustering effects of the non-stressed and stressed groups were revealed by score plots and score images calculated by principal component analysis (PCA). Then, loadings of PCA and competitive adaptive reweighted sampling (CARS) algorithm were employed to pick effective wavelengths (EWs) for discriminating non-stressed and stressed samples. Partial least squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were utilized to estimate the stressed tobacco plants status with different concentrations Hg solutions. The performances of those models were evaluated using confusion matrixes (CMes) and receiver operating characteristics (ROC) curves. Results demonstrated that PLS-DA models failed to offer relatively good result, and this algorithm was abandoned to classify the stressed and non-stressed groups of tobacco plants. Compared to LS-SVM model based on full spectra (FS-LS-SVM), the LS-SVM model established EWs selected by CARS (CARS-LS-SVM) carried 13 variables provided an accuracy of 100%, which was promising to achieve the qualitative discrimination of the non-stressed and stressed tobacco plants. Meanwhile, for revealing the discrepancy between 3 stressed groups of tobacco plants, the other FS-LS-SVM, PCA-LS-SVM, and CARS-LS-SVM models were setup and offered relatively low accuracies of 55.56%, 51.11% and 66.67%, respectively. Performance of those 3 LS-SVM discriminative models was also poorly performing to differentiate 3 stressed groups of tobacco plants, which might be caused by low concentration of heavy metal and similar canopy (especially in fresh leaves) of plant. The achievements of the research indicated that HSI coupled with machine learning methods had a powerful potential to discriminate tobacco plant stressed by heavy metal Hg.
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Affiliation(s)
- Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China
| | - Shiyan Fang
- College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, 22 Xinong Road, Yangling, Shaanxi 712100, PR China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, PR China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, PR China.
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Increasing Profitability and Monitoring Environmental Performance: A Case Study in the Agri-Food Industry through an Edge-IoT Platform. SUSTAINABILITY 2020. [DOI: 10.3390/su13010283] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Globalization has led to a new paradigm where the traditional industries, such as agriculture, employ vanguard technologies to broaden its possibilities into what is known as smart farming and the agri-food industry 4.0. This industry needs to adapt to the current market through an efficient use of resources while being environmentally friendly. The most commonly used approaches for analyzing efficiency and sustainability on farms are production efficiency based analyses, such as Data Envelopment Analysis and Stochastic Frontier Analysis, since they allow to see how efficient the outputs are generated regardless of the units of measurement of the inputs. This work presents a real scenario for making farms more profitable and sustainable through the analysis of the Data Envelopment Analysis and the application of the Internet of Things and Edge Computing. What makes this model interesting is that it allows monitoring the ambient conditions with real-time data from the different sensors that have been installed on the farm, minimizing costs and gaining robustness in the transmission of the data to the cloud with Edge Computing, and then to have a complete overview in terms of monthly resource efficiency through the Data Envelopment Analysis. The results show that including the costs of edge and non-edge data transfer have an impact on the efficiency. This small-scale study set the basis for a future test with many farms simultaneously.
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Fan S, Li J, Zhang Y, Tian X, Wang Q, He X, Zhang C, Huang W. On line detection of defective apples using computer vision system combined with deep learning methods. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.110102] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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48
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Cultivar Discrimination of Single Alfalfa ( Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis. SENSORS 2020; 20:s20226575. [PMID: 33217897 PMCID: PMC7698633 DOI: 10.3390/s20226575] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/08/2020] [Accepted: 11/13/2020] [Indexed: 12/18/2022]
Abstract
Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.
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de Medeiros AD, da Silva LJ, Ribeiro JPO, Ferreira KC, Rosas JTF, Santos AA, da Silva CB. Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging. SENSORS 2020; 20:s20154319. [PMID: 32756355 PMCID: PMC7435829 DOI: 10.3390/s20154319] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 12/13/2022]
Abstract
Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.
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Affiliation(s)
- André Dantas de Medeiros
- Agronomy Department, Federal University of Viçosa, Viçosa MG 36570-900, Brazil; (L.J.d.S.); (J.P.O.R.); (A.A.S.)
- Correspondence:
| | - Laércio Junio da Silva
- Agronomy Department, Federal University of Viçosa, Viçosa MG 36570-900, Brazil; (L.J.d.S.); (J.P.O.R.); (A.A.S.)
| | - João Paulo Oliveira Ribeiro
- Agronomy Department, Federal University of Viçosa, Viçosa MG 36570-900, Brazil; (L.J.d.S.); (J.P.O.R.); (A.A.S.)
| | | | | | - Abraão Almeida Santos
- Agronomy Department, Federal University of Viçosa, Viçosa MG 36570-900, Brazil; (L.J.d.S.); (J.P.O.R.); (A.A.S.)
- Entomology Department, Federal University of Viçosa, 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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Nguyen GN, Norton SL. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm. PLANTS (BASEL, SWITZERLAND) 2020; 9:E817. [PMID: 32610615 PMCID: PMC7411623 DOI: 10.3390/plants9070817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023]
Abstract
Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
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Affiliation(s)
- Giao N. Nguyen
- Australian Grains Genebank, Agriculture Victoria, 110 Natimuk Road, Horsham 3400, Australia;
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