Halal Food Identification from Product Ingredients using Machine Learning

Abstract

Halal food plays a critical role in the Islamic faith, as it represents food that is considered lawful according to Islamic law. Muslims are encouraged to eat only Halal foods to ensure that it aligns to their religious beliefs. However, locating and verifying Halal-certified foods can be challenging, especially for Muslim travelers unfamiliar with the local food market. Muslims ensure Halal foods that ingredients are prepared in accordance with Islamic Shariah law. Indicators like the Halal emblem have been used to help Muslims identify Halal food. Unfortunately, many packaged items are not Halal-certified. To address this issue, this study presents a method for detecting Halal items using deep learning and machine learning techniques. The purpose is to determine if an unknown product is Halal (legal) or Haram (Illegal) based on its ingredients. The suggested system examines packaged food product images and identifies the ingredients using the Yolo v5 algorithm. The text on the images of the ingredients is then recognized using optical character recognition (OCR). Various machine learning algorithms, artificial neural networks, and fuzzy interference rule are applied to determine the status of the food. The final outcome is to categorize Halal and Haram food products accurately. This approach has the potential to assist Muslim consumers in identifying Halal-certified products quickly and efficiently, particularly when traveling to new locations or encountering unfamiliar products. Using intelligent technology; this study presents a new and innovative technique for detecting Halal food. The result shows that the suggested approach is effective and it might be a useful tool for Muslim consumers in ensuring that the things they buy are compatible with their religious views