In recent years, the demand for food materials is reaching sky, thus making the artificial intelligence-based evaluation technique the research hot-spot now because the quality determination of these materials can be really difficult without proper understanding and experience. As a result, we have developed a classification model for artificial neural networks based on computer vision. On a primary dataset, we are utilizing a convolutional-based architecture to focus on shape and texture, which are the most crucial factors to experts. An example of a DNN (Deep Neural Network) is a convolutional neural network, which has been shown to be particularly effective in extracting features from input images. This project demonstrates that deep learning improved the accuracy of classification tasks over traditional image analysis methods. And thus, it can be inferred that it is a potential future tool for grading the food quality. We also intend to do an analysis of different machine learning algorithms available and compare it with our model.
AI University , Montana
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