Fresh Meat Classification on Web Application


  • Nur Nisha Camelia Syukri FKEE, Faculty of Electrical and Electronic 86400 Parit Raja, JohorEngineering
  • Danial Md Nor


Web App, Gradio, Image processing, Python, CNN


Meat is one of the most nutritious foods since it includes carbs, proteins, lipids, vitamins, and minerals.  The amount of freshness in meat is a significant component in determining meat quality.  Therefore, meat quality should be maintained so that consumers receive high-grade meat.  Meat quality is often determined visually by comparing genuine meat with reference photographs of each meat class. This procedure has flaws because it is subjective in nature and takes a long time. As a result, an image- processing-based automated system capable of detecting meat quality is required. In this work, a convolutional neural network approach is used to detect meat freshness. The meat utilised in the work is categorized into two types: fresh and spoiled. The goal of this work is to create an image processing system for meat quality classification by analysing digital pictures.  The system's implementation is to obtain equal measurement because different human examiners obtain different results, and to develop an image classifying system by implementing CNN algorithm with image processing technique and to evaluate the performance of the program classifier and prediction of validation data using training run results.




How to Cite

Syukri, N. N. C., & Danial Md Nor. (2023). Fresh Meat Classification on Web Application. Evolution in Electrical and Electronic Engineering, 4(2), 170–176. Retrieved from



Computer and Network