Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits
Background: Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-co...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
BioMed Central
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/180526 |
| _version_ | 1855532869758222336 |
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| author | Tharanya, M. Chakraborty, D. Pandravada, A. Babu, R. Gangashetti, M. Paidi, S. Choudhary, S. Sivasakthi, K. Anbazhagan, Krithika Vaditandra, B. Waininger, M. Weule, M. Hufnagel, E. Claußen, J. Vaněk, J. Wittenberg, T. Kholova, J. Gerth, S. |
| author_browse | Anbazhagan, Krithika Babu, R. Chakraborty, D. Choudhary, S. Claußen, J. Gangashetti, M. Gerth, S. Hufnagel, E. Kholova, J. Paidi, S. Pandravada, A. Sivasakthi, K. Tharanya, M. Vaditandra, B. Vaněk, J. Waininger, M. Weule, M. Wittenberg, T. |
| author_facet | Tharanya, M. Chakraborty, D. Pandravada, A. Babu, R. Gangashetti, M. Paidi, S. Choudhary, S. Sivasakthi, K. Anbazhagan, Krithika Vaditandra, B. Waininger, M. Weule, M. Hufnagel, E. Claußen, J. Vaněk, J. Wittenberg, T. Kholova, J. Gerth, S. |
| author_sort | Tharanya, M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Background: Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T1 chaffiness, (2) T2 chalky rice kernel percentage (CRK%), and (3) T3 head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity.
Results: The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R2 = 0.9987, RMSE = 1.302; CRK%: R2 = 0.9397, RMSE = 8.91; HRR%: R2 = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains.
Conclusions: Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops. |
| format | Journal Article |
| id | CGSpace180526 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | BioMed Central |
| publisherStr | BioMed Central |
| record_format | dspace |
| spelling | CGSpace1805262026-01-23T15:42:21Z Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits Tharanya, M. Chakraborty, D. Pandravada, A. Babu, R. Gangashetti, M. Paidi, S. Choudhary, S. Sivasakthi, K. Anbazhagan, Krithika Vaditandra, B. Waininger, M. Weule, M. Hufnagel, E. Claußen, J. Vaněk, J. Wittenberg, T. Kholova, J. Gerth, S. plant breeding rice x-ray irradiation Background: Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T1 chaffiness, (2) T2 chalky rice kernel percentage (CRK%), and (3) T3 head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity. Results: The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R2 = 0.9987, RMSE = 1.302; CRK%: R2 = 0.9397, RMSE = 8.91; HRR%: R2 = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains. Conclusions: Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops. 2025-07-09 2026-01-23T13:37:05Z 2026-01-23T13:37:05Z Journal Article https://hdl.handle.net/10568/180526 en Open Access BioMed Central Tharanya, M., Chakraborty, D., Pandravada, A., Babu, R., Gangashetti, M., Paidi, S., Choudhary, S., Sivasakthi, K., Anbazhagan, K., Vaditandra, B., Waininger, M., Weule, M., Hufnagel, E., Claußen, J., Vaněk, J., Wittenberg, T., Kholova, J. and Gerth, S. 2025. Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits. Plant Methods 21:94. |
| spellingShingle | plant breeding rice x-ray irradiation Tharanya, M. Chakraborty, D. Pandravada, A. Babu, R. Gangashetti, M. Paidi, S. Choudhary, S. Sivasakthi, K. Anbazhagan, Krithika Vaditandra, B. Waininger, M. Weule, M. Hufnagel, E. Claußen, J. Vaněk, J. Wittenberg, T. Kholova, J. Gerth, S. Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits |
| title | Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits |
| title_full | Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits |
| title_fullStr | Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits |
| title_full_unstemmed | Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits |
| title_short | Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits |
| title_sort | utilizing x ray radiography for non destructive assessment of paddy rice grain quality traits |
| topic | plant breeding rice x-ray irradiation |
| url | https://hdl.handle.net/10568/180526 |
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