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.
A Deep Learning project using Transfer Learning (EfficientNet) and Data Augmentation to classify three major rice leaf diseases (Bacterial Blight, Brown Spot, Leaf Smut). Provides a robust, high-accuracy model for early disease detection in precision agriculture.
Proyek ini adalah aplikasi web full-stack yang dirancang untuk deteksi otomatis penyakit pada daun padi. Pengguna dapat mengunggah gambar daun padi, dan sistem akan menganalisisnya menggunakan model machine learning yang telah dilatih sebelumnya.
A deep learning based disease detection system for rice leaves using images.
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.
Rice Leaf Disease Prediction System using Deep Learning, specifically leveraging the blessings of transfer learning.
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.
Scans picture of your rice crop leaf and tells dieseas, suggestion to improve and along with that genretes feedbacks onto it.
Rice Leaf Disease Classification Project Using Convolutional Neural Network (CNN)
Rice leaf disease detection model using a convolutional neural network model.
Using ResNet50 model in deep learning to predict rice leaf diseases. Using Kaggle's "Rice Leafs" dataset
A complete mobile image classification app built with Lambda Native framework, featuring knowledge distillation and GPU-accelerated training. The app can classify images into 5 custom categories with real-time inference on mobile devices.
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