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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.)

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

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Author(s):
Galletti, Patricia A. [1] ; Carvalho, Marcia E. A. [2] ; Hirai, Welinton Y. [3] ; Brancaglioni, Vivian A. [3] ; Arthur, Valter [4] ; Barboza da Silva, Clissia [4]
Total Authors: 6
Affiliation:
[1] Univ Sao Paulo, Dept Crop Sci, Coll Agr Luiz de Queiroz, Piracicaba - Brazil
[2] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Genet, Piracicaba - Brazil
[3] Univ Sao Paulo, Dept Exacts Sci, Coll Agr Luiz de Queiroz, Piracicaba - Brazil
[4] Univ Sao Paulo, Ctr Nucl Energy Agr, Lab Radiobiol & Environm, Piracicaba - Brazil
Total Affiliations: 4
Document type: Journal article
Source: FRONTIERS IN PLANT SCIENCE; v. 11, DEC 21 2020.
Web of Science Citations: 1
Abstract

Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries. (AU)

FAPESP's process: 18/24777-8 - Chlorophyll fluorescence and multispectral image analysis to evaluate the quality of carrot and tomato seeds
Grantee:Patrícia Aparecida Galletti
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 18/03807-6 - Multi-user equipment approved in grant 2017/15220-7: multiFocus digital radiography system
Grantee:Clíssia Barboza Mastrangelo
Support Opportunities: Multi-user Equipment Program
FAPESP's process: 18/03802-4 - Multi-user equipment approved in grant 2017/15220-7: imaging system VideoMeterLab
Grantee:Clíssia Barboza Mastrangelo
Support Opportunities: Multi-user Equipment Program
FAPESP's process: 18/03793-5 - Multi-user equipment approved in grant 2017/15220-7: imaging system SeedReporter camera spectral & colour
Grantee:Clíssia Barboza Mastrangelo
Support Opportunities: Multi-user Equipment Program
FAPESP's process: 18/01774-3 - Non-destructive image analysis methods for seed quality evaluation
Grantee:Clíssia Barboza Mastrangelo
Support Opportunities: Scholarships in Brazil - Young Researchers
FAPESP's process: 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation
Grantee:Clíssia Barboza Mastrangelo
Support Opportunities: Research Grants - Young Investigators Grants