Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201

Authors

  • Adi Dwifana Saputra Pradita University
  • Djarot Hindarto Universitas Pradita, Serpong, Indonesia
  • Handri Santoso Universitas Pradita, Serpong, Indonesia

DOI:

10.33395/sinkron.v8i1.11906

Keywords:

Rice Leaf Disease Detection, DenseNet121, DenseNet169, DenseNet201, Machine Learning, Deep Learning Training

Abstract

Rice is a plant that can grow in the tropics. This plant can produce food that can meet the needs of the people of a country. This plant can grow well if it is cared for properly. If the planting has used good care, such as providing adequate water, adding good fertilizer, it can be ascertained that it will produce a lot of rice fruit after harvesting. This often causes concern if rice growers have given good care but often produce less rice fruit because rice plants are attacked by various diseases. This is what makes the problem, that rice plants are attacked by diseases. Before spraying diseases or pests, farmers should have an understanding of diseases in rice. This makes farmers not wrong in choosing drugs for farmers' rice. It is very vulnerable if farmers do not know about the rice disease. Therefore, it is necessary to observe what types of rice diseases attack rice plants. Observations are not enough just to take pictures with a camera. But it is necessary to carry out further analysis of rice diseases. The presence of information technology is now able to recognize any type. One of the machine learning technologies is able to detect rice diseases. One of these branches of machine learning is deep learning. By using a dataset that focuses on rice disease, the model generated from deep learning training is able to detect rice disease. The purpose of this research is to predict disease in rice leaves using deep learning, namely DenseNet. Training using DenseNet, namely DenseNet121, DenseNet169 and DenseNet201. Accuracy using DenseNet121 reached 91.67%, DenseNet169 reached 90%, and DenseNet201 reached 88.33%. The model training time takes 24 seconds.

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How to Cite

Saputra, A. D. ., Hindarto, D. ., & Santoso, H. . (2023). Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 48-55. https://doi.org/10.33395/sinkron.v8i1.11906

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