Performance Comparison of VGG16, Mobilenet, And Xception Model Architecture in Rice Plant Leaf Identification
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Abstract
Rice is one of the world's most important staple foods. Rice is a staple food in most regions of the world, especially in Indonesia. Rice plant nutrition is one of the most important things in plant growth and development. Nutrient deficiencies in plants can affect the growth process and the quality of the plants when they are ready to be harvested. The dataset used in this research comes from the Kaggle platform, which has a total of 1190 datasets. The rice leaf images are divided into 2 classes, namely Sufficient and Deficient, which are tested with a ratio of 80% training data and 10% test data, and 10% as validation data. The model architecture used in this research is 3 VGG16, MobileNet and Xception using Jupyter and Google Collaboratory as tools. The tests were performed using 10 epochs and batch sizes of 32 and 64. The best accuracy results obtained are 78.15% and 76.47% for VGG16, 82.69% and 86.55% for MobileNet, 82.33% and 88.24% for Xception. Meanwhile, the best overall accuracy result was achieved by the Xception model at 88.24% with an input batch size of 32 and the tool used was Jupyter.
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