RESUMEN
BACKGROUND: In the contemporary food industry, accurate and rapid differentiation of oolong tea varieties holds paramount importance for traceability and quality control. However, achieving this remains a formidable challenge. This study addresses this lacuna by employing machine learning algorithms - namely support vector machines (SVMs) and convolutional neural networks (CNNs) - alongside computer vision techniques for the automated classification of oolong tea leaves based on visual attributes. RESULTS: An array of 13 distinct characteristics, encompassing color and texture, were identified from five unique oolong tea varieties. To fortify the robustness of the predictive models, data augmentation and image cropping methods were employed. A comparative analysis of SVM- and CNN-based models revealed that the ResNet50 model achieved a high Top-1 accuracy rate exceeding 93%. This robust performance substantiates the efficacy of the implemented methodology for rapid and precise oolong tea classification. CONCLUSION: The study elucidates that the integration of computer vision with machine learning algorithms constitutes a promising, non-invasive approach for the quick and accurate categorization of oolong tea varieties. The findings have significant ramifications for process monitoring, quality assurance, authenticity validation and adulteration detection within the tea industry. © 2023 Society of Chemical Industry.
