Rice Grain Species Classification using Machine Learning
Vaibhav Mishra
Classification of Rice Grain Species using Morphological Features
Project Overview
This project focuses on the classification of two rice species—Cammeo and Osmancik—using morphological features extracted from rice grain images. The goal is to implement and evaluate multiple machine learning models to achieve high classification accuracy.
Problem Statement
Rice classification plays a crucial role in quality control and variety identification in agriculture and the food industry. This project aims to:
Identify significant morphological features distinguishing the two species.
Implement machine learning techniques for classification.
Evaluate and compare the performance of various classification models.
Objectives
Preprocess the dataset and extract meaningful features.
Implement and evaluate multiple classification models.
Optimize models through hyperparameter tuning.
Analyze feature importance.
Visualize model performance using confusion matrices and ROC-AUC curves.
Dataset and Feature Description
The dataset includes morphological features:
Area: Number of pixels within the boundaries of the rice grain.
Perimeter: Circumference of the rice grain boundary.
Major Axis Length: Longest possible line across the rice grain.
Minor Axis Length: Shortest possible line across the rice grain.
Eccentricity: Measure of the rice grain’s roundness.
Convex Area: Pixel count of the smallest convex shell around the grain.
Extent: Ratio of the grain region to the bounding box area.
Machine Learning Models Used
Logistic Regression
Support Vector Machine (SVM)
Decision Tree
Random Forest
Gradient Boosting
K-Nearest Neighbors (KNN)
Neural Networks
Evaluation Metrics
Accuracy: Percentage of correct predictions.
Confusion Matrix: Breakdown of prediction results.
ROC-AUC: Performance measure using true positive and false positive rates.
Solution Methodology
1. Dataset Preprocessing
Loaded and cleaned the dataset (in .arff format).
Encoded the target variable (Class) into numeric values (0 for Cammeo, 1 for Osmancik).
Scaled feature values using StandardScaler.
2. Model Implementation
Implemented models using scikit-learn.
Evaluated models on an 80-20 train-test split.
3. Feature Importance Analysis
Extracted feature importance scores using Random Forest.
Applied Recursive Feature Elimination (RFE) for feature selection.
4. Hyperparameter Tuning
Optimized hyperparameters using GridSearchCV.
Used cross-validation to evaluate parameter combinations.
5. Visualization
Plotted confusion matrices, ROC curves, and feature importance charts.
Simulation Results
Feature Importance
The most significant features were:
Major Axis Length
Perimeter
Area
Convex Area
Eccentricity
Model Performance (After Hyperparameter Tuning)
Model Accuracy ROC-AUC Logistic Regression 0.9278 0.92 Support Vector Machine 0.9318 0.93 Decision Tree 0.8911 0.89 Random Forest 0.9265 0.95 Gradient Boosting 0.9304 0.94 KNN 0.9186 0.95 Neural Network 0.9304 0.95
Conclusion
Support Vector Machine (SVM) achieved the highest accuracy (93.7%) and ROC-AUC (0.97).
Area, Perimeter, and Major Axis Length were the most important features.
Hyperparameter tuning slightly improved model accuracy.
Visualization using confusion matrices and ROC curves provided deeper insights into model performance.
References
Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, 2011.
Hastie, Tibshirani, Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Dataset Source: Rice (Cammeo and Osmancik) [Dataset]. (2019). UCI Machine Learning Repository.
Supplementary Material
Full Python code and dataset available in supplementary files.
Contribution
Vaibhav Mishra (CH21B038) – 100% of the contributions.
This README provides an overview of the project, methodologies, and results. For implementation details, refer to the full project report and code files.
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Posted May 27, 2025
Classified rice species using machine learning models for high accuracy.
