Akademik Veri Yönetim Sistemi
Yüzüncü Yıl Üniversitesi Ziraat Fakültesi Tarım Bilimleri Dergisi, cilt.32, sa.4, ss.705-713, 2022 (Hakemli Dergi)
In modern digital agricultural applications, automatic identification and
diagnosis of plant diseases using artificial intelligence is becoming popular and
widespread. Deep learning is a promising tool in pattern recognition and machine
learning and it can be used to identify and classify diseases in paddy rice. In this
study, 2 different paddy rice diseases, including rice blast and brown spot, were
investigated in the district of İpsala in the province of Edirne between the 2020
and 2021 production seasons by collecting 1569 images. These diseases are very
common and important in Edirne province and surrounding rice production areas.
Therefore, practical methods are needed to identify and classify these two
diseases. A Convolutional Neural Network (CNN) model was created by applying
pre-processing techniques such as rescaling, rotation, and data augmentation to
the paddy rice disease images. The classification model was created in Google
Colab, which is a web-based Python editor using Tensorflow and Keras libraries.
The CNN model was able to classify rice blast and brown spot diseases with high
accuracy of 91.70%.