View Proposal

Proposer
Arulmurugan Ramu
Title
Meat Freshness Prediction System using Machine Learning
Goal
To develop a machine-learning system that predicts the freshness of meat from the data such as images, classifying samples into freshness levels to support faster and more consistent quality assessment.
Description
This project analyses meat images, freshness indicators, storage duration, and cold-chain conditions to identify spoilage risk and estimate remaining shelf life. Use Predictive techniques to classify freshness levels and support quality decisions to reduce waste and improve food safety across the supply chain
Resources
"1) Mahdi Rashvand, Yuqiao Ren, Da-Wen Sun, Julia Senge, Christian Krupitzer, Tobi Fadiji, Marta Sanzo Miró, Alex Shenfield, Nicholas J. Watson, Hongwei Zhang,Artificial intelligence for prediction of shelf-life of various food products: Recent advances and ongoing challenges,Trends in Food Science & Technology, https://www.sciencedirect.com/science/article/pii/S0924224425001256 "
Background
https://github.com/OguzhanUlucan/Meat-Quality-Assessment-Dataset
Url
Difficulty Level
Moderate
Ethical Approval
None
Number Of Students
1
Supervisor
Arulmurugan Ramu
Keywords
computer vision, food quality, image classification, ml, evaluation methodology, deep learning
Degrees
Bachelor of Science in Computational Sciences and Software Engineering
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