Plant diseases is one of the most influential factors in agricultural
production. It can affect product quality, quantity, or yield of
crops. Diagnosis of plant diseases is made mainly based on the experience
of farmers. This work is done based on the naked eye. It is often misleading,
time-consuming, and laborious. Machine learning methods based on
leaf images have been proposed to improve disease identification. Transfer
learning is accepted and proven to be effective. In this paper, we used
the transfer learning method to classify apple tree diseases. The research
data were used from the Fine-Grained Visual Categorization (FGVC7)
Kaggle PLANT PATHOLOGY 2020, expert-annotated to create a pilot
dataset for apple scab, cedar apple rust, multiple diseases, and healthy
leaves. The InceptionV3 architecture trained with the Adam optimizer
attained the highest validation accuracy.