Meat Freshness State Prediction Using a Novel Fifteen Layered Deep Convolutional Neural Network

The food marketplace needs a quick and reliable system for tracking and assessing the freshness of meat products. However, meat experiences a quick process of freshness deterioration, which leads to bacterial growth. As a result, the need for a reliable and quick way of monitoring and evaluating meat deterioration is growing urgent. By Considering these aspects, this paper proposes a Novel Fifteen Layered Deep Convolutional Neural Network (15L-DCNN) to predict the freshness state of meat with maximum accuracy. The model utilizes the Meat Freshness Image Dataset extracted from the KAGGLE machine learning repository. The Meat Freshness Image Dataset comprises three meat state classes, Fresh Meat, Half Fresh Meat, and Spoiled Meat, with 2269 meat images. The Meat Freshness Image Dataset have been subjected to data augmentation and performed with four operations: Random horizontal flip, Random vertical flip, zooming, and rotation. After data augmentation, the dataset ends with 6000 images. The Meat Freshness Image Dataset was splitted into 4800 training images, 600 validation images, and 600 testing images. The Meat Freshness training Images were subjected to the proposed 15L-DCNN and the same dataset was applied to EfficientNet, DenseNet, and ResNet Large models for evaluating the efficiency metrics. Python was adopted for the execution of NVidia Geforce Tesla V100 GPU workstation with 100 training iterations for a block size of 64. Experimental results show that the proposed model 15L-DCNN shows a maximum accuracy of 98.85%, Precision of 98.33%, Recall of 98.25%, misclassification rate of 1.15%, and FScore of 98.24% when compared with another convolutional neural network.