Combining deep learning and X-ray imaging technology to assess tomato seed quality
نویسندگان
چکیده
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 physiological tomato seeds. A convolutional neural network (CNN) called mask region (MaskRCNN) was also tested for its precision adequately classifying seeds into four categories. For purpose, images were taken 49 genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates 25 each genotype analyzed. These further seedling vigor-related traits independent trials. Correlation analysis revealed significant linear association between image-based variables. Most differed terms development performance considering trials, except LA 4046, 4043, LA4047, showed similar behavior. Our findings point out that with low opacity percentage damaged tissue high values living have greater quality. short, our work confirms reliability deep learning methodologies
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ژورنال
عنوان ژورنال: Scientia Agricola
سال: 2023
ISSN: ['1678-992X', '0103-9016']
DOI: https://doi.org/10.1590/1678-992x-2022-0121