Abstract
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.
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Huynh, H.X., Phan, C.A., Truong, L.T.T., Nguyen, H.T. (2021). Region of Interest Selection on Plant Disease. In: Cong Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications. ICCASA 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 409. Springer, Cham. https://doi.org/10.1007/978-3-030-93179-7_10
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