ResNet101 Model Performance Enhancement in Classifying Rice Diseases
with Leaf Images

ResNet101 Model Performance Enhancement in Classifying Rice Diseases
with Leaf Images

enhancement; performance; classification; disease; rice; resnet101

Indonesia is the fourth biggest rice producer in Asia with its production accounting for 35.4 million metric tons yearly. This
figure can increase unless rice crop failure is resolved. Identifying rice diseases, however, may serve as an approach to
minimizing the risk of crop failure. The classification to detect rice diseases was previously researched using ResNet101 method
with 100% accuracy. Despite this perfect accuracy, this approach does not come without an issue, where the prediction is not
yet optimal for each label and loss results which are regarded as too high due to overfitting. Departing from this issue, this
research aims to improve the model by reducing the layer complexity of the model and comparing two layers structures of the
model, two different data, and the ResNet101 model. The performance resulting from the model could be enhanced with the
structuring of simple architectural layers. Despite the small quantity of dataset, the model performance can yield 100%
accuracy in the classification of rice diseases with a loss value of 2.91%. The model performance in this research experienced
a 2.7% increase at the loss value and it could accurately classify the type of rice diseases according to leaf images on each
label. The problem solved by this research is that ResNet101 is able to classify rice disease accurately even with a small amount
of data by utilizing the appropriate layer arrangement with data requirements. In addition, the overfitting that occurred in
previous research can also be resolved properly. This matter proves that the correlation between the layers of the model with
the amount of data is very influential

Galih Wasis Wicaksono1
, Andreawan2

Universitas Muhammadiyah Malang

26-03-2023

Fajar bagus W

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Indonesia

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