Multi-user equipment approved in grant 2017/15220-7: imaging system SeedReporter camera spectral & colour

• Grant number: 18/03793-5
• Support Opportunities: Multi-user Equipment Program
• Duration: April 01, 2018 - March 31, 2025
• Field of knowledge: Agronomical Sciences - Agronomy - Crop Science
• Principal Investigator: Clíssia Barboza Mastrangelo
• Grantee: Clíssia Barboza Mastrangelo
• Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated research grant: 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation, AP.JP
• EMU web page: http://www.esalq.usp.br/departamentos/lpv/labimagem/Plano%20de%20Gestão_Seed%20Reporter.pdf
• Type of equipment: Caracterização de Materiais - Imageamento - Ótico; Caracterização e Análises de Amostras - Biomédica - Tecidos
• Manufacturer: PhenoVation Life Sciences
• Model: SeedReporter Camera Spectral & Colour.

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)

Scientific publications

(5)

(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)

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, AUG 2021. (18/03802-4, 18/01774-3, 18/03793-5, 17/15220-7)

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, DEC 21 2020. (18/24777-8, 18/03807-6, 18/03802-4, 18/03793-5, 18/01774-3, 17/15220-7)

FABIANO FRANÇA-SILVA; SILVIO MOURE CICERO; FRANCISCO GUILHIEN GOMES-JUNIOR; ANDRÉ DANTAS MEDEIROS; JOSÉ DE BARROS FRANÇA-NETO; DENISE CUNHA FERNANDES SANTOS DIAS. Quantification of chlorophyll fluorescence in soybean seeds by multispectral images and their relationship with physiological potential. JOURNAL OF SEED SCIENCE, v. 44, 2022-07-08. (18/03793-5, 18/03802-4)

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, SEP 8 2021. (18/03793-5, 18/03802-4, 17/15220-7, 18/01774-3)

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, APR 2021. (18/01774-3, 18/03793-5, 18/03807-6, 17/15220-7)