The image data of rice leaf disease total 120jpg images with 3 classes [Brown spot, leaf smut, bacterial blight] and each class contain 40jpg images.
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Updated
Sep 5, 2022 - Jupyter Notebook
The image data of rice leaf disease total 120jpg images with 3 classes [Brown spot, leaf smut, bacterial blight] and each class contain 40jpg images.
This project aims to detect diseases on the leaves of rice plants in Indonesia using the Convolutional Neural Network (CNN) Inception V3 method to design a classification model and produce a high level of accuracy.
A deep learning based disease detection system for rice leaves using images.
Rice Leaf Disease Prediction System using Deep Learning, specifically leveraging the blessings of transfer learning.
Rice leaf disease detection model using a convolutional neural network model.
A deep learning project for detecting and classifying rice leaf diseases using the ResNet-50 architecture. Includes data augmentation, transfer learning, and evaluation metrics such as accuracy, precision, recall, and confusion matrix. Achieves over 98% accuracy in classifying four classes: Bacterial Blight, Blast, Brown Spot, and Tungro.
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