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.
Abstract (translated)
这项研究采用了一种创新的方法对各种类型的波斯大米进行分类，利用基于图像的深度学习技术，强调了日常技术在食品分类中的实际应用。我们认识到波斯大米的多样性和其烹饪重要性，并充分利用卷积神经网络（CNN）的特性，通过微调ResNet模型进行不同大米品种的准确识别，并采用U-Net架构对批量图片中的大米颗粒进行精确分割。这种双重方法框架允许实现单粒谷物分类和全面分析散装大米样本，解决了评估大米质量的两个关键方面。利用消费者级智能手机拍摄的照片反映了现实情况，即个人可以利用这项技术协助购物和烹饪。这个数据集包括各种大米类型在自然条件下拍摄的照片，没有专业照明或设备，呈现了一个具有挑战性但实用的分类问题。我们的研究结果表明，可以使用非专业图像进行食品分类，深度学习模型（如ResNet和U-Net）可以适应日常物品和纹理的细微差别。这项研究为该领域提供了关于图像为基础的深度学习在日常生活中的应用，特别是为了提高消费者在食品选择中的体验和知识的见解。此外，它还开辟了将这种方法扩展到其他食品类别和实际应用领域的途径，强调可访问技术在连接复杂计算方法和日常任务之间的作用。
URL
https://arxiv.org/abs/2404.13555
