AI- Colormilk: Colorimetric AI for Microbial Quality Assessment of Milk

The microbial quality of milk is essential for ensuring the safety, nutritional integrity, and shelf life of dairy products. Traditional methods for testing microbial contamination, such as culture-based techniques, are time-consuming, labor-intensive, and prone to delays. To address these challenges, this study explores the use of rapid colorimetric methods combined with artificial intelligence (AI) for detecting microbial contamination in raw and processed milk. The colorimetric techniques involve detecting color changes caused by microbial activity, providing a simple and rapid means of assessing milk quality. A machine learning model, trained on a dataset of RGB and HSV color features, was used to classify milk samples into contamination levels: Low, Medium, and High. The models, including Random Forest and Support Vector Machines (SVM), demonstrated high accuracy in predicting microbial contamination. A web application was developed to enable real-time milk quality analysis by allowing users to input data for instant contamination level predictions. The results show that the combination of colorimetric methods and AI can effectively improve microbial detection, ensuring higher food safety standards in the dairy industry. The system provides a cost-effective and efficient solution for quality control, enabling proactive management of milk contamination. Future work includes expanding the dataset, integrating advanced sensor technologies, and incorporating real-time IoT monitoring for continuous quality assurance in dairy production.