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Non-destructive image analysis methods for seed quality evaluation

Grant number: 17/15220-7
Support type:Research Grants - Young Investigators Grants
Duration: February 01, 2018 - August 31, 2023
Field of knowledge:Agronomical Sciences - Agronomy - Crop Science
Principal researcher:Clíssia Barboza da Silva
Grantee:Clíssia Barboza da Silva
Home Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Assoc. researchers: Agide Gimenez Marassi ; Alberto Tannús ; Fernando Fernandes Paiva ; Francisco Guilhien Gomes Junior ; Julio Marcos Filho ; Roberval Daiton Vieira ; Silvio Moure Cicero
Associated grant(s):18/03807-6 - Multi-user equipment approved in grant 2017/15220-7: multiFocus digital radiography system, AP.EMU
18/03802-4 - Multi-user equipment approved in grant 2017/15220-7: imaging system VideoMeterLab, AP.EMU
18/03793-5 - Multi-user equipment approved in grant 2017/15220-7: imaging system SeedReporter camera spectral & colour, AP.EMU
Associated scholarship(s):21/01329-2 - Effect of the treatment of soybean seeds with micronutrients in association with ultrasound assessed by multispectral imaging, BP.MS
20/12686-8 - Application of analytical techniques of magnetic resonance imaging and multispectral imaging for peanut seed evaluation, BP.IC
20/09407-0 - Relationship between the chlorophyll fluorescence during germination and the physiological potential of soybean seeds, BP.MS
+ associated scholarships 20/04852-5 - Application of analytical techniques of magnetic resonance imaging and multispectral imaging for peanut seed evaluation, BP.IC
19/04127-1 - Application of analytical techniques of magnetic resonance imaging and multispectral image to evaluate Jatropha curcas L. seeds, BP.IC
18/24777-8 - Chlorophyll fluorescence and multispectral image analysis to evaluate the quality of carrot and tomato seeds, BP.MS
18/01774-3 - Non-destructive image analysis methods for seed quality evaluation, BP.JP - associated scholarships

Abstract

The growing demand for seeds in Brazil, especially regarding to crops of economic importance, requires the constant improvement of the parameters for quality evaluation, aiming maximizing production and the performance of the agricultural sector in the economy, both national and international. Therefore, the evaluation of seed quality using non-destructive image analysis techniques is of great interest, since objective information can be achieved, in a relatively short period of time, with less human interference and with great portability potential. Following through with the "thematic project" - Seed imaging analysis in seed technology research - financed by FAPESP (Grant number: 06/57900-0), the objective of this research project is to continue studies on non-destructive methods for seed quality evaluation with the use and improvement of recent techniques of x-rays and magnetic resonance, as well as the introduction and the establishment in Brazil of imaging techniques based on multispectral analysis and chlorophyll fluorescence. The expected results of this project are to find patterns optical images by using advanced techniques which can characterize the quality patterns of carrot, tomato, physic nut (Jatropha curcas L.) and peanut seeds in continuation of previous studies on "seed imaging analysis" that our team has been working in recent years, and with that, to strengthen and expand innovative approaches that include new lines of research with inter-institutional and international collaborations. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
Articles published in other media outlets (0 total):
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Scientific publications (13)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GALLETTI, PATRICIA A.; CARVALHO, MARCIA E. A.; HIRAI, WELINTON Y.; BRANCAGLIONI, VIVIAN A.; ARTHUR, VALTER; BARBOZA DA SILVA, CLISSIA. Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases. FRONTIERS IN PLANT SCIENCE, v. 11, . (18/24777-8, 18/03802-4, 18/03793-5, 18/01774-3, 17/15220-7, 18/03807-6)
MASTRANGELO, THIAGO; DA SILVA, FABIANO FRANCA; MASCARIN, GABRIEL MOURA; DA SILVA, CLISSIA BARBOZA. Multispectral imaging for quality control of laboratory-reared Anastrepha fraterculus (Diptera: Tephritidae) pupae. Journal of Applied Entomology, . (18/03802-4, 17/15220-7)
DA SILVA, CLISSIA BARBOZA; NAVES SILVA, ALYSSON ALEXANDER; BARROSO, GEOVANNY; YAMAMOTO, PEDRO TAKAO; ARTHUR, VALTER; MOTTA TOLEDO, CLAUDIO FABIANO; MASTRANGELO, THIAGO DE ARAUJO. Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain. FOODS, v. 10, n. 4, . (18/01774-3, 18/03793-5, 18/03807-6, 17/15220-7)
MEDEIROS, ANDRE DANTAS DE; SILVA, LAERCIO JUNIO DA; RIBEIRO, JOAO PAULO OLIVEIRA; FERREIRA, KAMYLLA CALZOLARI; ROSAS, JORGE TADEU FIM; SANTOS, ABRAAO ALMEIDA; SILVA, CLISSIA BARBOZA DA. Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging. SENSORS, v. 20, n. 15, . (17/15220-7)
DE MEDEIROS, ANDRE DANTAS; CAPOBIANGO, NAYARA PEREIRA; DA SILVA, JOSE MARIA; DA SILVA, LAERCIO JUNIO; DA SILVA, CLISSIA BARBOZA; FERNANDES DOS SANTOS DIAS, DENISE CUNHA. Interactive machine learning for soybean seed and seedling quality classification. SCIENTIFIC REPORTS, v. 10, n. 1, . (17/15220-7)
BARBOZA DA SILVA, CLISSIA; MARCOS-FILHO, JULIO. Storage performance of primed bell pepper seeds with 24-Epibrassinolide. AGRONOMY JOURNAL, v. 112, n. 2, p. 948-960, . (11/07842-1, 17/15220-7)
DA SILVA, CLISSIA BARBOZA; OLIVEIRA, NIELSEN MOREIRA; AMARAL DE CARVALHO, MARCIA EUGENIA; DE MEDEIROS, ANDRE DANTAS; NOGUEIRA, MARINA DE LIMA; DOS REIS, ANDRE RODRIGUES. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality. SCIENTIFIC REPORTS, v. 11, n. 1, . (18/03793-5, 18/03802-4, 17/15220-7, 18/01774-3)
BERNARDES, RODRIGO CUPERTINO; LIMA, MARIA AUGUSTA PEREIRA; GUEDES, RAUL NARCISO CARVALHO; DA SILVA, CLISSIA BARBOZA; MARTINS, GUSTAVO FERREIRA. Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis. SENSORS, v. 21, n. 9, . (17/15220-7)
DE MEDEIROS, ANDRE DANTAS; BERNARDES, RODRIGO CUPERTINO; DA SILVA, LAERCIO JUNIO; LEMOS DE FREITAS, BRUNO ANTONIO; FERNANDES DOS SANTOS DIAS, DENISE CUNHA; DA SILVA, CLISSIA BARBOZA. Deep learning-based approach using X-ray images for classifying Crambe abyssinica seed quality. INDUSTRIAL CROPS AND PRODUCTS, v. 164, . (17/15220-7)
OLIVEIRA, NIELSEN MOREIRA; DE MEDEIROS, ANDRE DANTAS; NOGUEIRA, MARINA DE LIMA; ARTHUR, VALTER; MASTRANGELO, THIAGO DE ARAUJO; DA SILVA, CLISSIA BARBOZA. Hormetic effects of low-dose gamma rays in soybean seeds and seedlings: A detection technique using optical sensors. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 187, . (18/03802-4, 18/01774-3, 18/03793-5, 17/15220-7)
DA SILVA, CLISSIA BARBOZA; MARTINS BIANCHINI, VITOR DE JESUS; DE MEDEIROS, ANDRE DANTAS; DUARTE DE MORAES, MARIA HELOISA; MARASSI, AGIDE GIMENEZ; TANNUS, ALBERTO. A novel approach for Jatropha curcas seed health analysis based on multispectral and resonance imaging techniques. INDUSTRIAL CROPS AND PRODUCTS, v. 161, . (17/15220-7, 19/04127-1, 18/01774-3, 18/03802-4)
FRANCA-SILVA, FABIANO; QUEIROZ REGO, CARLOS HENRIQUE; GUILHIEN GOMES-JUNIOR, FRANCISCO; DUARTE DE MORAES, MARIA HELOISA; DE MEDEIROS, ANDRE DANTAS; DA SILVA, CLISSIA BARBOZA. Detection ofDrechslera avenae(Eidam) Sharif [Helminthosporium avenae(Eidam)] in Black Oat Seeds (Avena strigosaSchreb) Using Multispectral Imaging. SENSORS, v. 20, n. 12, . (17/15220-7, 18/03802-4)
MARTINS BIANCHINI, VITOR DE JESUS; MASCARIN, GABRIEL MOURA; APARECIDA SANTOS SILVA, LUCIA CRISTINA; ARTHUR, VALTER; CARSTENSEN, JENS MICHAEL; BOELT, BIRTE; DA SILVA, CLISSIA BARBOZA. Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality. PLANT METHODS, v. 17, n. 1, . (18/03807-6, 19/04127-1, 17/15220-7, 18/03802-4, 18/01774-3)

Please report errors in scientific publications list by writing to: cdi@fapesp.br.