Combining deep learning and X-ray imaging technology to assess tomato seed quality
ABSTRACT Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) ca...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidade de São Paulo
2023-08-01
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| Series: | Scientia Agricola |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100605&lng=en&tlng=en |
| _version_ | 1797743238653673472 |
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| author | Herika Paula Pessoa Mariane Gonçalves Ferreira Copati Alcinei Mistico Azevedo Françoise Dalprá Dariva Gabriella Queiroz de Almeida Carlos Nick Gomes |
| author_facet | Herika Paula Pessoa Mariane Gonçalves Ferreira Copati Alcinei Mistico Azevedo Françoise Dalprá Dariva Gabriella Queiroz de Almeida Carlos Nick Gomes |
| author_sort | Herika Paula Pessoa |
| collection | DOAJ |
| description | ABSTRACT Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds. |
| first_indexed | 2024-03-12T14:52:40Z |
| format | Article |
| id | doaj.art-618eb976c46f4c61a6aee9fdca809585 |
| institution | Directory Open Access Journal |
| issn | 1678-992X |
| language | English |
| last_indexed | 2024-03-12T14:52:40Z |
| publishDate | 2023-08-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Scientia Agricola |
| spelling | doaj.art-618eb976c46f4c61a6aee9fdca8095852023-08-15T07:45:48ZengUniversidade de São PauloScientia Agricola1678-992X2023-08-018010.1590/1678-992x-2022-0121Combining deep learning and X-ray imaging technology to assess tomato seed qualityHerika Paula Pessoahttps://orcid.org/0000-0001-5284-684XMariane Gonçalves Ferreira Copatihttps://orcid.org/0000-0001-7908-5622Alcinei Mistico Azevedohttps://orcid.org/0000-0001-5196-0851Françoise Dalprá Darivahttps://orcid.org/0000-0002-4259-5212Gabriella Queiroz de Almeidahttps://orcid.org/0000-0002-0340-7346Carlos Nick Gomeshttps://orcid.org/0000-0001-5990-255XABSTRACT Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100605&lng=en&tlng=enSolanum lycopersicumcomputer visionhigh throughputgermination |
| spellingShingle | Herika Paula Pessoa Mariane Gonçalves Ferreira Copati Alcinei Mistico Azevedo Françoise Dalprá Dariva Gabriella Queiroz de Almeida Carlos Nick Gomes Combining deep learning and X-ray imaging technology to assess tomato seed quality Scientia Agricola Solanum lycopersicum computer vision high throughput germination |
| title | Combining deep learning and X-ray imaging technology to assess tomato seed quality |
| title_full | Combining deep learning and X-ray imaging technology to assess tomato seed quality |
| title_fullStr | Combining deep learning and X-ray imaging technology to assess tomato seed quality |
| title_full_unstemmed | Combining deep learning and X-ray imaging technology to assess tomato seed quality |
| title_short | Combining deep learning and X-ray imaging technology to assess tomato seed quality |
| title_sort | combining deep learning and x ray imaging technology to assess tomato seed quality |
| topic | Solanum lycopersicum computer vision high throughput germination |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162023000100605&lng=en&tlng=en |
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