https://github.com/soroushesnaashari/rice-images-classification-with-cnn-using-tensorflow

A Convolutional Neural Network (CNN) project using "TensorFlow" framework to classify Rice images into five types
https://github.com/soroushesnaashari/rice-images-classification-with-cnn-using-tensorflow

cnn keras tensorflow

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A Convolutional Neural Network (CNN) project using "TensorFlow" framework to classify Rice images into five types

• Host: GitHub
• URL: https://github.com/soroushesnaashari/rice-images-classification-with-cnn-using-tensorflow
• Owner: soroushesnaashari
• Created: 2025-01-10T16:24:19.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-03T20:22:46.000Z (over 1 year ago)
• Last Synced: 2025-03-03T21:26:53.949Z (over 1 year ago)
• Topics: cnn, keras, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 2.32 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Rice Classification

[![](Image.jpg)](https://unsplash.com/photos/aerial-view-photography-of-rice-crops-during-daytime-cusz0Bg-5mQ)

### Overview

This project demonstrates how to build and train a Convolutional Neural Network (CNN) using TensorFlow to classify images of rice. The goal is to develop an automated image classification system that can accurately distinguish between different types of rice, a task that can be crucial in agricultural research and quality control. The project walks through data preprocessing, model design, training, evaluation and result visualization.




### Project Workflow

The project follows an end-to-end workflow:

1. **Data Acquisition & Preparation**

   - ***Dataset Collection:*** Gather rice images from various sources.

   - ***Data Preprocessing:*** Resize images, normalize pixel values, and (if needed) apply data augmentation to increase dataset diversity.

2. **Model Design & Implementation**

   - ***CNN Architecture:*** Develop a CNN model using TensorFlow. The model typically includes several convolutional layers, pooling layers, and dense layers to extract features and        perform classification.

   - ***Compilation:*** Set up the model with appropriate loss functions and optimizers.

3. **Training & Evaluation**

   - ***Training:*** Train the model on the prepared dataset while monitoring performance on a validation set.

   - ***Evaluation:*** Assess the model using metrics such as accuracy, loss curves, and confusion matrices to understand its strengths and weaknesses.

4. **Results Visualization & Analysis**

   - Plot training/validation curves to visualize the learning process.

   - Display sample predictions along with actual labels to evaluate performance qualitatively.




### Key Features

- **End-to-End Pipeline:** From data loading and preprocessing to model training and evaluation.

- **Custom CNN Architecture:** Designed specifically for rice image classification.

- **TensorFlow Integration:** Utilizes TensorFlow’s high-level APIs for model building and training.

- **Data Augmentation:** Techniques implemented (if applicable) to improve model robustness by artificially expanding the dataset.

- **Comprehensive Evaluation:** Detailed analysis of model performance with metrics and visualizations.




### Results

- **Model Performance:** The trained CNN achieves competitive accuracy in classifying rice images (e.g., reaching an accuracy of **`over 99% on both models`** on the training set).

- **Visual Insights:** Training and validation loss/accuracy curves are generated to monitor overfitting and learning progress.

- **Error Analysis:** Confusion matrices and misclassified examples provide insight into the model's decision-making and help guide future improvements.




### Repository Contents

- **`rice_classification.ipynb`**: Jupyter Notebook with full code, visualizations, and explanations.

- **`Data`:** Contains the [Original Dataset](https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset/data) and you can see the cleaned dataset in notebook.

- **`README.md`:** Project documentation.




### How to Contribute

Contributions are welcome! If you'd like to improve the project or add new features:

1. **Fork the repository.**

2. **Create a new branch.**

3. **Make your changes and submit a pull request.**