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.
Keywords: Rice Leaf Disease Detection; DenseNet121; DenseNet169;
DenseNet201; Machine Learning; Deep Learning Training;