APPLYING MACHINE LEARNING TO IDENTIFY COUNTERFEIT FOODS

Currently, the shelves of shops and supermarkets are filled with food that people consume daily, many products coming from abroad. However, all these useful for human body, do they meet standards? In this article, we will talk about how to identify low-quality using modern machine learning. Recognition classification images text based on learning can be a key technology in fight against low[1]quality food. Automatic image recognition product information enable end customers counterfeit accurately quickly by comparing them trained templates. it is clear does not apply processing enterprises. production, non-standard used reduce cost product. Manufacturers change their replacing higher quality lower ones. They may use confusing terms label mislead you. When buying serving products, consumers suffer different ways. First, getting nutrients need, adulterated foods safe health, also an economic loss consumers. We evaluate technical feasibility components fraud detection architecture real-world scenario, including models distinguish multiple each other. It allows you control circulation at state level, thereby protecting consumer purchasing potentially dangerous goods. MobileNetV2 model multiclass evaluated received angles.