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Submitted
January 8, 2023
Published
March 31, 2023
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Abstract

Vegetables are foodstuffs of plant origin that can be consumed fresh and have various health benefits. However, not a few people do not know the types of vegetables and will find it difficult to find the vegetables they want. This research aims to make it easier for people to find vegetables by classifying them. Researchers developed a model using the Convolutional Neural Network (CNN) method with a total of 15 datasets with a total of 3000 image data. Researchers conducted training datasets with 3 types of epochs, including 20 epochs, 50 epochs and 100 epochs. The training produces accuracy and training loss, with the highest accuracy belonging to Epoch 50 and Epoch 100 and the lowest level of training loss is owned by Epoch 100 with a total of 0.609. However, after the model was deployed, the accuracy results obtained were not as high as the tests conducted on Google Colab. Tests were carried out on several objects, including carrots with an accuracy of 69%, cabbage with an accuracy of 53%, and papaya with an accuracy of 82%. The difference in accuracy results may be caused by objects that are less identical to the datasets or can also be caused by imperfect models. Even so, this application can already be used to classify types of vegetables.

Keywords

Android Convolutional Neural Network (CNN) Epoch Google Colab Vegetables

Article Details

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