Summary of Cell Phone Image-based Persian Rice Detection and Classification Using Deep Learning Techniques, by Mahmood Saeedi Kelishami and Amin Saeidi Kelishami and Sajjad Saeedi Kelishami
Cell Phone Image-Based Persian Rice Detection and Classification Using Deep Learning Techniques
by Mahmood Saeedi kelishami, Amin Saeidi Kelishami, Sajjad Saeedi Kelishami
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a deep learning-based approach to classify different types of Persian rice using image-based techniques, highlighting its practical application in food categorization. The authors fine-tune a ResNet model for accurate individual grain classification and employ a U-Net architecture for precise segmentation of rice grains in bulk images. This dual-methodology framework addresses two crucial aspects of rice quality assessment: individual grain classification and comprehensive analysis of bulk rice samples. The dataset, comprising various rice types photographed under natural conditions with consumer-grade cell phones, presents a challenging yet practical classification problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses deep learning models to classify different types of Persian rice using images taken with regular cameras or phones. This is helpful for people who want to buy the right type of rice at the grocery store. The authors tested their approach by fine-tuning a special kind of neural network called ResNet and another one called U-Net. They used these networks to identify individual grains of rice and group them together based on how they look. This study shows that it’s possible to use ordinary images to classify food, which can make it easier for people to choose the right type of rice or other foods. |
Keywords
* Artificial intelligence * Classification * Deep learning * Fine tuning * Neural network * Resnet
