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

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: FRONTIERS IN PLANT SCIENCE; v. 11, DEC 21 2020.
• Citações Web of Science: 1

Resumo

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)

• Processo FAPESP: 18/24777-8 - Fluorescência de clorofila e análise multiespectral de imagens para avaliação da qualidade de sementes de cenoura e tomate
• Beneficiário: Patrícia Aparecida Galletti
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 18/03807-6 - EMU concedido no processo 2017/15220-7: multiFocus digital radiography system
• Beneficiário: Clíssia Barboza Mastrangelo
• Modalidade de apoio: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
• Processo FAPESP: 18/03802-4 - EMU concedido no processo 2017/15220-7: sistema de imagem VideoMeterLab
• Beneficiário: Clíssia Barboza Mastrangelo
• Modalidade de apoio: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
• Processo FAPESP: 18/03793-5 - EMU concedido no processo 2017/15220-7: sistema de imagem SeedReporter câmera spectral & colour
• Beneficiário: Clíssia Barboza Mastrangelo
• Modalidade de apoio: Auxílio à Pesquisa - Programa Equipamentos Multiusuários
• Processo FAPESP: 18/01774-3 - Métodos de análise de imagens não destrutivos para avaliação da qualidade de sementes
• Beneficiário: Clíssia Barboza Mastrangelo
• Modalidade de apoio: Bolsas no Brasil - Jovens Pesquisadores
• Processo FAPESP: 17/15220-7 - Métodos de análise de imagens não destrutivos para avaliação da qualidade de sementes
• Beneficiário: Clíssia Barboza Mastrangelo
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores