Recipe Recognition for Indian Cuisine: Deep Learning vs Machine Learning
Duvvuru Manoj Kumar, Tiriveedi Hari Kiran, Annavarapu Ganesh, Appajodu Rama krishna, Muhammed Swalih, Mirsah vt
Image Classification, Food Recognition, CNN, MobileNet, KNN, Random Forest, Machine learning, Deep Learning
In this research, we compared the Machine Learning and Deep Learning Algorithms on Indian Dataset. We Collected Indian food Images of more than 20 categories and applied Machine learning and Deep learning algorithms which includes MobileNet, KNN, CNN, and Random Forest, to the Indian food dataset and to train the algorithms and test them on that data. We measured the performance of each algorithm in the terms of accuracy, precision, recall and F1 score, while taking into account Identification of Indian food was the main priority. In contrast, to compare and analyze the computational efficiency and resource requirements for different algorithms, taking into account the model complexity, and the training model. Via thorough evaluation using the test data, each algorithm's accuracy in the food Recognition model is Comprehensively examined. impressively, MobileNet turned up as the top performer, achieving an impressive accuracy score of 92.1%, followed by CNN at 79.1%. On the other hand, Machine learning algorithms which are KNN and Randomforest display relatively lower accuracies of 25.5% and 33.3%, respectively. These results provide valuable insights into the efficacy of diverse machine learning and Deep learning techniques in food Recognition.
Article Details
Unique Paper ID: 164735

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1607 - 1615
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