Rice Grain Classification using Machine Learning & Deep Learning

An image-based rice grain classification system that leverages Computer Vision, Machine Learning, and Deep Learning techniques to automatically identify different rice varieties. The project evaluates multiple classification algorithms and compares their performance to determine the most effective model for accurate grain recognition.

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Overview

Rice variety identification plays an important role in agricultural quality control, grading, and supply chain management. Manual classification is time-consuming and prone to errors, making automated Computer Vision solutions increasingly valuable.

This project develops an intelligent rice grain classification system using image preprocessing, feature extraction, and Machine Learning and Deep Learning algorithms. Multiple models are trained and evaluated to identify the most accurate approach for classifying rice grain varieties.

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Key Features

• Image-based rice grain classification
• Computer Vision preprocessing techniques
• Feature extraction and data preparation
• Deep Learning using Convolutional Neural Networks (CNN)
• Machine Learning model comparison
• Performance evaluation using multiple metrics
• Automated prediction of rice grain varieties

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Project Workflow

Rice Grain Images
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Image Preprocessing
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Feature Extraction
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Dataset Preparation
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Model Training
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Performance Evaluation
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Rice Variety Prediction

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System Architecture

Rice Image Dataset
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Image Preprocessing
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Feature Extraction
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Classification Models
 ├── CNN
 ├── K-Nearest Neighbors
 ├── Decision Tree
 ├── Support Vector Machine
 ├── Logistic Regression
 └── Linear Regression
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Performance Comparison
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Predicted Rice Variety

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Methodology

The project follows the following pipeline:

• Image preprocessing
• Data cleaning
• Feature extraction
• Dataset preparation
• Model training
• Performance evaluation
• Comparative analysis of Machine Learning and Deep Learning models

The models are evaluated using standard classification metrics to determine the best-performing algorithm.

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Technology Stack

Programming Language

• Python

Computer Vision

• OpenCV

Deep Learning

• TensorFlow
• Keras
• Convolutional Neural Networks (CNN)

Machine Learning

• Scikit-learn
• K-Nearest Neighbors (KNN)
• Decision Tree
• Support Vector Machine (SVM)
• Logistic Regression
• Linear Regression

Data Processing

• NumPy
• Pandas

Visualization

• Matplotlib

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Performance

The project compares multiple Machine Learning and Deep Learning models for rice grain classification.

Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

The CNN model achieved the best overall classification performance among the evaluated models.

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Project Structure

Rice-Grain-Classification/
│
├── dataset/
├── notebooks/
├── models/
├── results/
├── requirements.txt
├── README.md
└── LICENSE

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Installation

Clone the repository

git clone https://github.com/GowthamRavuri01/rice-grain-classification.git

Navigate to the project directory

cd rice-grain-classification

Install dependencies

pip install -r requirements.txt

Run the project

Launch the Jupyter Notebook or Python script to preprocess the dataset, train the models, and evaluate classification performance.

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Future Improvements

• Transfer Learning using ResNet and EfficientNet
• Real-time rice grain classification
• Mobile application deployment
• Model optimization for edge devices
• Explainable AI (Grad-CAM) for prediction visualization
• Cloud deployment

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Author

Gowtham Ravuri

AI Engineer | Computer Vision Engineer | Software Engineer

• GitHub: https://github.com/GowthamRavuri01
• LinkedIn: https://www.linkedin.com/in/gowthamravuri

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License

This project is licensed under the MIT License.