Identification of Paddy Rice Diseases Using Deep Convolutional Neural Networks

Abstract

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%.