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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.03214 |
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| _version_ | 1866916644293443584 |
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| author | Tyagi, Ankush Motwani, Dhruv Dabhi, Vipul K. Prajapati, Harshadkumar B. |
| author_facet | Tyagi, Ankush Motwani, Dhruv Dabhi, Vipul K. Prajapati, Harshadkumar B. |
| contents | The rice grain quality can be determined from its size and chalkiness. The traditional approach to measure the rice grain size involves manual inspection, which is inefficient and leads to inconsistent results. To address this issue, an image processing based approach is proposed and developed in this research. The approach takes image of rice grains as input and outputs the number of rice grains and size of each rice grain. The different steps, such as extraction of region of interest, segmentation of rice grains, and sub-contours removal, involved in the proposed approach are discussed. The approach was tested on rice grain images captured from different height using mobile phone camera. The obtained results show that the proposed approach successfully detected 95\% of the rice grains and achieved 90\% accuracy for length and width measurement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rice Grain Size Measurement using Image Processing Tyagi, Ankush Motwani, Dhruv Dabhi, Vipul K. Prajapati, Harshadkumar B. Image and Video Processing Computer Vision and Pattern Recognition The rice grain quality can be determined from its size and chalkiness. The traditional approach to measure the rice grain size involves manual inspection, which is inefficient and leads to inconsistent results. To address this issue, an image processing based approach is proposed and developed in this research. The approach takes image of rice grains as input and outputs the number of rice grains and size of each rice grain. The different steps, such as extraction of region of interest, segmentation of rice grains, and sub-contours removal, involved in the proposed approach are discussed. The approach was tested on rice grain images captured from different height using mobile phone camera. The obtained results show that the proposed approach successfully detected 95\% of the rice grains and achieved 90\% accuracy for length and width measurement. |
| title | Rice Grain Size Measurement using Image Processing |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.03214 |