This project aims to detect diseases on the rice leaf using the Convolutional Neural Network (CNN) Inception V3 method to design a classification model and produce a high level of accuracy. Classification will be carried out with three classes in the form of blast, leaf blight, and tungro with the results of accuracy in predicting the classification of disease types on the leaves of pads seen using the results of model testing accuracy, MAE plots, and MSE plots.
The dataset was obtained from the Kaggle dataset repository site in .csv format uploaded by Tedi Setiadi and can be accessed through the Penyakit_Daun_Padi_Indonesia folder or the following Link. The dataset is an image of rice plant leaves taken from fields in the Southeast Sulawesi area, Indonesia. The dataset consists of 240 image data of infected rice leaves and is divided into 3 classes based on the type of rice leaf disease, namely blast, leaf blight and tungro with each class consisting of 80 image data.
For a more detailed explanation please refer to the paper that you can access through this Link.
Python using Google Collab
Syenira Sheila
- LinkedIn: @SyeniraSheila
- Github: @syenirasheila
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Copyright © 2022 Syenira Sheila.
This project is MIT licensed.