Rice Classification

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 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.