Abstract
Due to the COVID-19, self-catering captured the interest of many people. This paper proposes a novel mobile application, which can share recipes and recognition material to help individuals with low prior cooking skill. It offers good, practical knowledge and can help to build cooperative teams in the cooking community among novice cooks. Choosing the ingredients for cooking can be difficult. This is especially true because of Thai vegetables look similar such as white and sweet basil particularly for new cooks not familiar with their other characteristics. This research introduces a mobile application, Kin Rai Dee App, which is based on sharing recipes and recognition material by using Roboflow with a pretrained model. To develop Thai vegetable image classification in our mobile application, the Convolutional Neural Network technique and a Thai vegetable dataset is used to evaluate the performance of our classification model. This dataset is composed of two sources including (1) Thai herb dataset from Kaggle website and (2) our own images. Therefore, there are totally 12 classes in the Thai vegetable dataset with image’s resolutions of 224 × 224 pixels. The result for image training is implemented through machine learning and Roboflow methods. The experiments process has training results accuracy at 85% and testing result at 15% in both models. The performance of our model has proven that it can achieve the result with confidence values 100% and 99.21% for specific Thai vegetables.
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Phophan, T., Khuthanon, R., Chantamit-o-Pas, P. (2022). Thai Recipe Retrieval Application Using Convolutional Neural Network. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2022. Lecture Notes in Computer Science, vol 13492. Springer, Cham. https://doi.org/10.1007/978-3-031-16538-2_14
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