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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

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Author(s):
Martins Bianchini, Vitor de Jesus [1] ; Mascarin, Gabriel Moura [2] ; Aparecida Santos Silva, Lucia Cristina [3] ; Arthur, Valter [3] ; Carstensen, Jens Michael [4] ; Boelt, Birte [5] ; da Silva, Clissia Barboza [3]
Total Authors: 7
Affiliation:
[1] Univ Sao Paulo, Dept Crop Sci, Coll Agr Luiz de Queiroz, 11 Padua Dias Ave, Box 9, BR-13418900 Piracicaba, SP - Brazil
[2] Brazilian Agr Res Corp, Embrapa Environm, Lab Environm Microbiol, Rodovia SP 340, Km 127-5, BR-13820000 Jaguariuna - Brazil
[3] Univ Sao Paulo, Lab Radiobiol & Environm, Ctr Nucl Energy Agr, 303 Centenario Ave, BR-13416000 Piracicaba, SP - Brazil
[4] Tech Univ Denmark, DK-2800 Lyngby - Denmark
[5] Aarhus Univ, Dept Agroecol Sci & Technol, DK-4200 Slagelse - Denmark
Total Affiliations: 5
Document type: Journal article
Source: PLANT METHODS; v. 17, n. 1 JAN 26 2021.
Web of Science Citations: 2
Abstract

BackgroundThe 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.ResultsWe 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.ConclusionsMultispectral 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. (AU)

FAPESP's process: 18/03807-6 - Multi-user equipment approved in grant 2017/15220-7: multiFocus digital radiography system
Grantee:Clíssia Barboza da Silva
Support type: Multi-user Equipment Program
FAPESP's process: 19/04127-1 - Application of analytical techniques of magnetic resonance imaging and multispectral image to evaluate Jatropha curcas L. seeds
Grantee:Vitor de Jesus Martins Bianchini
Support type: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation
Grantee:Clíssia Barboza da Silva
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 18/03802-4 - Multi-user equipment approved in grant 2017/15220-7: imaging system VideoMeterLab
Grantee:Clíssia Barboza da Silva
Support type: Multi-user Equipment Program
FAPESP's process: 18/01774-3 - Non-destructive image analysis methods for seed quality evaluation
Grantee:Clíssia Barboza da Silva
Support type: Scholarships in Brazil - Young Researchers