Potato plants are plants that are beneficial to humans. Like other plants in
general, potato plants also have diseases; if this disease is not treated
immediately, there will be a significant decrease in food production.
Therefore, it is necessary to detect diseases quickly and precisely so that
disease control can be carried out effectively and efficiently. Classification
of potato leaf disease can be done directly. Still, the symptoms cannot always
explain the type of disease that attacks potato leaves because there are many
types of diseases with symptoms that look the same. Humans also have
deficiencies in determining the results of identification of potato leaf
disease, so sometimes the results of identification between individuals can be
different. Therefore, the use of Deep Learning for the classification process
of potato leaf disease is expected to shorten the time and have a high
classification accuracy. This study uses a deep learning method with the
DenseNet201 architecture. The choice to use the DenseNet201 algorithm in this
study is because the model can identify important features of potato leaves and
recognize early signs of emerging diseases. This study aimed to evaluate the
effectiveness of the transfer learning method with the DenseNet201 architecture
in increasing the classification accuracy of potato leaf disease compared to
traditional classification methods. This study uses two types of scenarios,
namely, comparing the number of dropouts and comparing the three optimizers.
This test produces the best model using dropout 0.1 and Adam optimizer with an
accuracy of 99.5% for training, 95.2% for validation, and 96% for the confusion
matrix. In this study, using data testing, as many as 40 images were tested
into the model that has been built. The test results on this model resulted in
a new accuracy for classifying potato leaf disease, namely 92.5%.

通过使用 Deep Learning 方法和 DenseNet201 架构，本研究旨在评估在诊断土豆叶病方面，迁移学习方法相比传统分类方法是否能提高分类准确度，实验结果显示，该方法在训练集上的准确率为 99.5％，验证集为 95.2％，混淆矩阵为 96％，在测试集上的准确率为 92.5％。