IoT Based Meat Freshness Classification Using Deep Learning

Meat quality and safety are critical concerns in the food industry, especially for products like beef and mutton, which are susceptible to spoilage and fraud. Traditional methods of assessing meat freshness and species classification, such as manual inspection, are often inefficient and prone to error. This paper introduces a novel Internet of Things (IoT) system that integrates gas sensors and advanced machine learning models, particularly deep learning, to address these issues effectively. The system combines image-based classification using a custom Convolutional Neural Network (CNN) with gas sensor data to provide a comprehensive, real-time solution for the classification of both beef and mutton in terms of species and freshness. The custom Convolutional Neural Network (CNN) was trained on a dataset comprising 9,928 images, 6,672 of which were utilized for meat freshness classification and 3,256 for meat species classification. The model achieved a classification accuracy of 99%, surpassing the performance of other models, including ResNet-50, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). It is important to note that identical training and validation datasets were employed across all four models, ensuring a consistent and equitable comparison of their performance. The custom CNN showed a clear advantage in handling the complex image data, particularly in distinguishing between beef and mutton species as well as their freshness levels. The system incorporates three gas sensors-MQ135, MQ4, and MQ136-to detect gases such as ammonia (NH3), methane (CH4), and hydrogen sulfide (H2S), which are released during the spoilage of meat. These gas sensor readings are utilized specifically for the classification of meat freshness. The system provides real-time feedback via LED indicators: green for fresh meat, yellow for meat that is neither fresh nor fully rotten, and red for spoiled meat. The results of the classification, based on both image data and gas readings, are displayed on an LED screen. This offers an efficient, scalable, and practical solution for real-time quality monitoring of beef and mutton. This integrated approach significantly improves the accuracy, reliability, and efficiency of meat safety management within the food supply chain.