Application of Vision AI for Assessing the Usability of Packaged Household Cooking Oil Based on Visual Images

Abstract

The quality of cooking oil plays an important role in maintaining public health; therefore, practical and accessible methods are needed to assess its feasibility. This study aims to implement a Vision Artificial Intelligence (Vision AI) approach based on Convolutional Neural Networks (CNN), specifically MobileNetV2 with transfer learning, to classify the feasibility of household packaged cooking oil using visual images. The dataset consists of cooking oil images categorized into two classes: usable and non-usable. The research stages include image acquisition, data preprocessing, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show that the proposed model achieves an accuracy of 83.33% on the test dataset, with a precision of 78.57%, recall of 91.67%, and F1-score of 84.62%. Furthermore, evaluation using 5-fold cross-validation yields an average accuracy of 86.6% in the best scenario, indicating good model generalization, although a lower average accuracy of 58.1% is observed under more challenging data distributions. The training process demonstrates a stable learning pattern, characterized by increasing accuracy and decreasing loss values across epochs. Overall, these findings indicate that the Vision AI approach has strong potential to be utilized as a non-destructive decision-support system for assessing cooking oil feasibility based on visual image analysis. However, further improvements are required, particularly in enhancing model robustness, reducing false positive errors, and improving performance consistency across varying data distributions.