Cell Phone Image-Based Persian Rice Detection and Classification Using Deep Learning Techniques

TL;DR

This paper presents a practical deep learning framework using cell phone images for classifying and segmenting Persian rice varieties, demonstrating real-world applicability in food identification and quality assessment.

Contribution

It introduces a dual deep learning approach with CNN and U-Net models for rice classification and segmentation using non-professional images, highlighting practical food recognition applications.

Findings

Deep learning models can accurately classify rice types from cell phone images.

Non-professional images are feasible for food classification tasks.

The approach enhances consumer food selection and quality assessment.

Abstract

This study introduces an innovative approach to classifying various types of Persian rice using image-based deep learning techniques, highlighting the practical application of everyday technology in food categorization. Recognizing the diversity of Persian rice and its culinary significance, we leveraged the capabilities of convolutional neural networks (CNNs), specifically by fine-tuning a ResNet model for accurate identification of different rice varieties and employing a U-Net architecture for precise segmentation of rice grains in bulk images. This dual-methodology framework allows for both individual grain classification and comprehensive analysis of bulk rice samples, addressing two crucial aspects of rice quality assessment. Utilizing images captured with consumer-grade cell phones reflects a realistic scenario in which individuals can leverage this technology for assistance with grocery shopping and meal preparation. The dataset, comprising various rice types photographed under natural conditions without professional lighting or equipment, presents a challenging yet practical classification problem. Our findings demonstrate the feasibility of using non-professional images for food classification and the potential of deep learning models, like ResNet and U-Net, to adapt to the nuances of everyday objects and textures. This study contributes to the field by providing insights into the applicability of image-based deep learning in daily life, specifically for enhancing consumer experiences and knowledge in food selection. Furthermore, it opens avenues for extending this approach to other food categories and practical applications, emphasizing the role of accessible technology in bridging the gap between sophisticated computational methods and everyday tasks.