Convolution Neural Network-Based Meat Quality Checker Using Different Optimizers

This research focuses on classifying the meat quality using a convolution neural network (CNN). The study mainly focused on classifying the meat quality using the microscopic camera without adding chemicals. In this proposed research, the meat is checked in two different ways: using the pH method and image processing using deep learning. Our method has the advantage of double-step verification of the meat by using pH and image processing. The quality of meat prediction by the proposed method is 97% accurate when compared to the conventional way of categorizing meat. The performance of analyzing the quality of meat is further improved by taking more significant number of data set collections with different angle of meat image. It enriches the CNN algorithm performance with tuned hyperparameters.